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Life, death, and self: Fundamental questions of primitive cognition viewed through the lens of body plasticity and synthetic organisms
Abstract
Central to the study of cognition is being able to specify the Subject that is making decisions and owning memories and preferences. However, all real cognitive agents are made of parts (such as brains made of cells). The integration of many active subunits into a coherent Self appearing at a larger scale of organization is one of the fundamental questions of evolutionary cognitive science. Typical biological model systems, whether basal or advanced, have a static anatomical structure which obscures important aspects of the mind-body relationship. Recent advances in bioengineering now make it possible to assemble, disassemble, and recombine biological structures at the cell, organ, and whole organism levels. Regenerative biology and controlled chimerism reveal that studies of cognition in intact, “standard”, evolved animal bodies are just a narrow slice of a much bigger and as-yet largely unexplored reality: the incredible plasticity of dynamic morphogenesis of biological forms that house and support diverse types of cognition. The ability to produce living organisms in novel configurations makes clear that traditional concepts, such as body, organism, genetic lineage, death, and memory are not as well-defined as commonly thought, and need considerable revision to account for the possible spectrum of living entities. Here, I review fascinating examples of experimental biology illustrating that the boundaries demarcating somatic and cognitive Selves are fluid, providing an opportunity to sharpen inquiries about how evolution exploits physical forces for multi-scale cognition. Developmental (pre-neural) bioelectricity contributes a novel perspective on how the dynamic control of growth and form of the body evolved into sophisticated cognitive capabilities. Most importantly, the development of functional biobots – synthetic living machines with behavioral capacity – provides a roadmap for greatly expanding our understanding of the origin and capacities of cognition in all of its possible material implementations, especially those that emerge de novo, with no lengthy evolutionary history of matching behavioral programs to bodyplan. Viewing fundamental questions through the lens of new, constructed living forms will have diverse impacts, not only in basic evolutionary biology and cognitive science, but also in regenerative medicine of the brain and in artificial intelligence.
... This has an obvious correlate: optimal problem solving will typically be achieved by groups, not individuals (Graesser et al. 2018;Dubova, Galesic, and Goldstone 2022). The parallel between this socialscale phenomenon and the requirements for cooperation between phenotypically diverse individuals in the construction of a multicellular organism (Strassmann and Queller 2010) are similarly obvious, leading to the proposal that all intel-ligence is fundamentally composite or collective (Levin 2021;2022, Fields andLevin 2022). We therefore suggest that "human intelligence" is properly thought of as a composite (HI, OI), where OI is some "other intelligence" that may be human but may also be as simple as a physical system (a notebook, a laptop) supporting stigmergic memory. ...
... Understanding the Umwelten of other organisms is, however, part of any biologist's job description, just as understanding the Umwelten of diverse other people is a crucial requirement for living in human society. It involves not just understanding what another organism can perceive and do, but critically, what another organism is capable of remembering or caring about (Levin 2021;. As Nagel's work emphasizes, understanding another being's Uumwelt in this 3rd-person sense is not the same as experiencing it oneself. ...
... Security-system issues, e.g., trust, are clearly relevant in any such setting, as are representations of other agent's goals and abilities (Dafoe et al. 2020). Here again, the analogy between AI and biology is obvious (Levin 2021;Fields and Levin 2022). ...
Replicating or exceeding human intelligence, not just in particular domains but in general, has always been a major goal of Artificial Intelligence (AI). We argue here that “human intelligence” is not only ill-defined, but often conflated with broader aspects of human psychology. Standard arguments for replicating it are morally unacceptable. We then suggest a reframing: that the proper goal of AI is not to replicate humans, but to complement them by creating diverse intelligences capable of collaborating with humans. This goal renders issues of theory of mind, empathy, and caring, or community engagement, central to AI. It also challenges AI to better understand the circumstances in which human intelligence, including human moral intelligence, fails.
... It is extremely popular and widespread in contemporary scientific and philosophical thinking, the basic tenet being that both natural agency and cognition are special varieties of algorithmic computation. Computationalism formulates agential and cognitive phenomena in terms of (often complicated, nonlinear, and heavily feedback-driven) input-output information processing (see, Baluška and Levin, 2016;Levin, 2021, for a particularly strong and explicit example). In this framework, goaldirectedness-sometimes unironically called "machine wanting" (McShea, 2013)-tends to be explained by some kind of cybernetic homeostatic regulation (e.g., McShea, 2012McShea, , 2013McShea, , 2016Lee and McShea, 2020). ...
... For this reason, computation is not considered exclusive to living systems. Researchers in the pancomputationalist paradigm see agency and cognition as continuous with non-linear and selforganizing information processing outside the living world (see, for example, Levin, 2021;Bongard and Levin, 2023). On this view, there is no fundamental boundary between the realms of the living and the non-living, between biology and computer engineering. ...
... While predictive processing keeps an open mind toward aspects of large worlds that cannot be formalized, strongly (pan)computationalist approaches to agency and cognition (see, for example, Baluška and Levin, 2016;Levin, 2021;Bongard and Levin, 2023) fail to acknowledge or address the basic insight that relevance realization cannot be of an algorithmic nature. Basically, these approaches only work within small worlds, where (as we have established here) there is no problem of relevance. ...
The way organismic agents come to know the world, and the way algorithms solve problems, are fundamentally different. The most sensible course of action for an organism does not simply follow from logical rules of inference. Before it can even use such rules, the organism must tackle the problem of relevance. It must turn ill-defined problems into well-defined ones, turn semantics into syntax. This ability to realize relevance is present in all organisms, from bacteria to humans. It lies at the root of organismic agency, cognition, and consciousness, arising from the particular autopoietic, anticipatory, and adaptive organization of living beings. In this article, we show that the process of relevance realization is beyond formalization. It cannot be captured completely by algorithmic approaches. This implies that organismic agency (and hence cognition as well as consciousness) are at heart not computational in nature. Instead, we show how the process of relevance is realized by an adaptive and emergent triadic dialectic (a trialectic), which manifests as a metabolic and ecological-evolutionary co-constructive dynamic. This results in a meliorative process that enables an agent to continuously keep a grip on its arena, its reality. To be alive means to make sense of one’s world. This kind of embodied ecological rationality is a fundamental aspect of life, and a key characteristic that sets it apart from non-living matter.
... In life, genetic and epigenetic networks precisely coordinate the expression of genes; however, most networks fail in organismal death (1), yet some continue to function despite loss of the "whole," as certain organs, tissues, and cells survive well, as exemplified by organ harvesting of cadavers for transplantation and by immortal HeLa and MM1S cells that are propagated by scientists for medical research. In many ways, this raises some of the same fundamental questions of multiscale biology as the inverse process, embryogenesis, in which the large-scale integrated self first arises as an emerging property from a collective of individual cells (2,3). ...
... Recent studies offer support for the idea of a "transformation into something new." For instance, skin cells taken from frog embryos, when given the opportunity to regain multicellularity in vitro, have been transformed into novel entities (3). These cells reassemble, utilize cilia for movement, repair damage, interact with their environment, and display spontaneous behavior while repurposing some of their original functions. ...
... Xenobots were used as a model to investigate what happens when a higher-level entity dies but its cells are still alive: what do they do? In this aquatic model, cells, unlike in mammals, continue to live, offering insights into the conatus (tendency to continue to exist) after an organism's death (3,25). ...
Significant knowledge gaps exist regarding the responses of cells, tissues, and organs to organismal death. Examining the survival mechanisms influenced by metabolism and environment, this research has the potential to transform regenerative medicine, redefine legal death, and provide insights into life's physiological limits, paralleling inquiries in embryogenesis.
... It is extremely popular and widespread in contemporary scientific and philosophical thinking, the basic tenet being that both natural agency and cognition are special varieties of algorithmic computation. Computationalism formulates agential and cognitive phenomena in terms of (often complicated, nonlinear, and heavily feedback-driven) input-output information processing (see, Baluška and Levin, 2016;Levin, 2021, for a particularly strong and explicit example). In this framework, goaldirectedness-sometimes unironically called "machine wanting" (McShea, 2013)-tends to be explained by some kind of cybernetic homeostatic regulation (e.g., McShea, 2012McShea, , 2013McShea, , 2016Lee and McShea, 2020). ...
... For this reason, computation is not considered exclusive to living systems. Researchers in the pancomputationalist paradigm see agency and cognition as continuous with non-linear and selforganizing information processing outside the living world (see, for example, Levin, 2021;Bongard and Levin, 2023). On this view, there is no fundamental boundary between the realms of the living and the non-living, between biology and computer engineering. ...
... While predictive processing keeps an open mind toward aspects of large worlds that cannot be formalized, strongly (pan)computationalist approaches to agency and cognition (see, for example, Baluška and Levin, 2016;Levin, 2021;Bongard and Levin, 2023) fail to acknowledge or address the basic insight that relevance realization cannot be of an algorithmic nature. Basically, these approaches only work within small worlds, where (as we have established here) there is no problem of relevance. ...
The way organismic agents come to know the world, and the way algorithms solve problems, are fundamentally different. The most sensible course of action for an organism does not simply follow from logical rules of inference. Before it can even use such rules, the organism must tackle the problem of relevance. It must turn ill-defined problems into well-defined ones, turn semantics into syntax. This ability to realize relevance is present in all organisms, from bacteria to humans. It lies at the root of organismic agency, cognition, and consciousness, arising from the particular autopoietic, anticipatory, and adaptive organization of living beings. In this paper, we show that the process of relevance realization is beyond formalization. It cannot be captured completely by algorithmic approaches. This implies that organismic agency (and hence cognition as well as consciousness) are at heart not computational in nature. Instead, we show how the process of relevance is realized by an adaptive and emergent triadic dialectic (a trialectic), which manifests as a metabolic and ecological-evolutionary co-constructive dynamic. This results in a meliorative process that enables an agent to continuously keep a grip on its arena, its reality. To be alive means to make sense of one's world. This kind of embodied ecological rationality is a fundamental aspect of life, and a key characteristic that sets it apart from non-living matter.
... BC deals with the fundamental processes necessary to sustain the organism, including cell-to-cell signaling, bioelectricity, etc. These processes evolved long before the nervous system existed (Lyon 2006;Keijzer et al. 2013;Baluška, Levin 2016;Levin 2019Levin , 2021Levin , 2022. BC treats these processes as cognitive in nature. ...
... The classical parameters of intelligence, like problem-solving, memory, and decision-making, are not bound to the nervous system. These attributes of intelligence are exhibited in genes in cells to organs in a biological system, and to non-living entities (Levin 2021). Levin defines the self as "a coherent system emerging within a set of integrated parts that serve as the functional owner of associations, memories, and preferences and acts to accomplish goals in specific problem spaces where those goals belong to the collective and not to any individual components" (Levin 2022, 40). ...
... The unified self dissipates when one looks at a biological system through a microscopic lens. Varela addressed this point with the neologism of meshed selves or the "Selfless Self" (1990), and Levin (2021) christened this problem of the unified self as the dark matter of cognitive science. ...
... BC deals with the fundamental processes necessary to sustain the organism, including cell-to-cell signaling, bioelectricity, etc. These processes evolved long before the nervous system existed (Lyon 2006;Keijzer et al. 2013;Baluška, Levin 2016;Levin 2019Levin , 2021Levin , 2022. BC treats these processes as cognitive in nature. ...
... The classical parameters of intelligence, like problem-solving, memory, and decision-making, are not bound to the nervous system. These attributes of intelligence are exhibited in genes in cells to organs in a biological system, and to non-living entities (Levin 2021). Levin defines the self as "a coherent system emerging within a set of integrated parts that serve as the functional owner of associations, memories, and preferences and acts to accomplish goals in specific problem spaces where those goals belong to the collective and not to any individual components" (Levin 2022, 40). ...
... The unified self dissipates when one looks at a biological system through a microscopic lens. Varela addressed this point with the neologism of meshed selves or the "Selfless Self" (1990), and Levin (2021) christened this problem of the unified self as the dark matter of cognitive science. ...
In the history of philosophy, the concept of self has been perennially elusive. The philosophical quest to understand the self is rife with phenomenological and metaphysical analyses, often overlooking other kinds of selves present in the biological realm. To systematically explore this question of non-human selves, I categorize the literature on philosophical and biological notions of self into the biogenic, the zoogenic, and the anthropogenic approaches to self. This article attempts to chart the genesis, the continuum, and the lowest bound of the self. Further, I enumerate challenges in developing a biogenic approach to self or taking the concept of self all the way down in the phylogenetic tree.
... This cross-generational stability serves the purpose of maximising the chances of survival for subsequent generations by maintaining those phenotypic characteristics that proved beneficial in the past. Genes are part of this internalised species memory, though perhaps not the most important part; patterns of bioelectric signalling seem to be just as important (Levin, 2021) (see also point 5 below). However, (to repeat) this does not mean that the outcome of developmental processes is 'predetermined', as illustrated impressively by unpredicted morphological novelties emerging from targeted manipulation. ...
... On the contrary, the development of a particular body architecture is itself guided by cognition. Cells act together drawing on memory that is stored not in the genes but in the patterns of the bioelectric signals by which cells communicate with one another (Clawson & Levin, 2022;Levin & Yuste, 2022;Levin, 2021Levin, , 2014aLevin, , 2014bLevin & Dennett 2020). ...
... The structure of the epigenetic landscape can be manipulated. Michael Levin and colleagues have shown, in a number of rather mind-blowing experiments, that manipulation of bioelectrical patterns of cell signalling in salamander, frog and planarian flatworm embryos prompts the development of entirely novel anatomies (Davies & Levin, 2023;Levin, 2021). Levin speaks of` 'somatic plasticity' (Levin, 2021(Levin, , 2019. ...
This article discusses the metaphysics of development and evolution. Which most fundamental assumptions about the structure of reality underlie our thinking about development and evolution? Against the backdrop of major lines of thought in the history of western metaphysics, I argue that the characteristic disregard of development in neo-Darwinist evolutionary theory is due to an underlying view of reality in terms of things (thing ontology), and that putting development back into evolution, as intended by the Extended Evolutionary Synthesis, requires understanding reality in terms of processes (process ontology). I show how a metaphysical paradigm shift from thing ontology to process ontology, and a philosophy of biology informed accordingly by process ontology (process biology), can advance our understanding of development and evolution.
... Much work has addressed the evolution of developmental mechanisms, the evolvability of specific architectures, and the emergent complexity of epigenesis [2][3][4][5][6]. Moreover, recent work has begun to emphasize the active, cybernetic, problem-solving capacities of this process beyond feedforward emergence [7][8][9][10][11] and explore ways in which evolution increases the functional intelligence of cellular collectives [12][13][14]. Here, I focus on the complementary side of the evolution-intelligence feedback loop. ...
... require any goal-directedness in the evolutionary process at the large scale. Instead, it builds on progress in connectionist machine learning [34,35,[46][47][48][49][50], basal cognition [12,[51][52][53][54][55], and an extension of neuroscience to developmental bioelectricity [56][57][58] to reveal how the collective intelligence of cells serves as an affordance for evolution. In that, it is complementary to current efforts to understand how network and other generic mathematical and physical properties affect the evolutionary process [59][60][61][62][63][64][65][66][67][68]. ...
... Thus, it emphasizes morphogenesis as a computational process, not merely the emergent outcome of highly parallel local rules that could be dealt with by established complexity theory tools. Moreover, it expands the concept of intelligent behavior across a key invariant: effective navigation of diverse problem spaces, which includes problem-solving in physiological, metabolic, transcriptional, and anatomical spaces [12,13,86]. With these expansions in hand, it becomes possible to see how evolution pivoted some of the same strategies across these domains, and to then explore the implications that this multiscale competency architecture has for the evolutionary process itself. ...
A critical aspect of evolution is the layer of developmental physiology that operates between the genotype and the anatomical phenotype. While much work has addressed the evolution of developmental mechanisms and the evolvability of specific genetic architectures with emergent complexity, one aspect has not been sufficiently explored: the implications of morphogenetic problem-solving competencies for the evolutionary process itself. The cells that evolution works with are not passive components: rather, they have numerous capabilities for behavior because they derive from ancestral unicellular organisms with rich repertoires. In multicellular organisms, these capabilities must be tamed, and can be exploited, by the evolutionary process. Specifically, biological structures have a multiscale competency architecture where cells, tissues, and organs exhibit regulative plasticity—the ability to adjust to perturbations such as external injury or internal modifications and still accomplish specific adaptive tasks across metabolic, transcriptional, physiological, and anatomical problem spaces. Here, I review examples illustrating how physiological circuits guiding cellular collective behavior impart computational properties to the agential material that serves as substrate for the evolutionary process. I then explore the ways in which the collective intelligence of cells during morphogenesis affect evolution, providing a new perspective on the evolutionary search process. This key feature of the physiological software of life helps explain the remarkable speed and robustness of biological evolution, and sheds new light on the relationship between genomes and functional anatomical phenotypes.
... Each level and component of a living system are simultaneously observers and hackers, interpreting and taking advantage of different aspects of the mechanisms in their microenvironments, in parallel. Life polycomputes because it is a set of overlapping, competing, cooperating nested dolls, each of which is doing the best it can to predict and exploit its microenvironment [47][48][49][50][51][52][53]. ...
... Neither process supports any kind of clean bright line that separates the cognitive human being from the "just physics" of a quiescent oocyte or the "true grounded knowledge" from the statistically driven speech behavior of babies and some AIs, etc. ( Table 1). All of these, like the process of slowly changing a being from a caterpillar to a butterfly [47], show that familiar categories in fact represent the poles of a spectrum of highly diverse mixed properties. The interoperability of life [47,[83][84][85] enables chimeras at all levels of an organization, which provide a continuum of every possible combination of features from supposedly distinct categories (Table 1), making it impossible to objectively classify either natural or artificial chimeras [29,38]. ...
... All of these, like the process of slowly changing a being from a caterpillar to a butterfly [47], show that familiar categories in fact represent the poles of a spectrum of highly diverse mixed properties. The interoperability of life [47,[83][84][85] enables chimeras at all levels of an organization, which provide a continuum of every possible combination of features from supposedly distinct categories (Table 1), making it impossible to objectively classify either natural or artificial chimeras [29,38]. It is becoming increasingly apparent that the departmental, funding, and publication distinctions between disciplines (e.g., neuroscience and cell biology, are much more of a practical consequence of our cognitive and logistical limitations than the reflection of a deep underlying distinction. ...
The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as “polycomputing”—the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.
... The second approach is basal cognition and concerns both the phylogenetic origins of learning and the target-directed activity of organisms. This creates a continuum between the ancient information processing mechanisms and the activity directed at more complex objectives (Levin, 2021). This approach is based on a similarity mechanism, for example, similarities between neuronal and non-neuronal organisms. ...
... This approach is based on a similarity mechanism, for example, similarities between neuronal and non-neuronal organisms. Such mechanisms are crucial to broadening the total understanding of cognition (Levin, 2021). Basal cognition regards cognitive processes in simple organisms and challenges the anthropogenic approach to cognition. ...
Philosophers have suggested that plants are cognitive agents, sparking an intense debate across theoretical and empirical perspectives. Some critics argue that plants lack the necessary abilities to be cognitive and claim that “cognitive” is not literal in plant cognition. This paper does not assert that plants are cognitive agents but urges philosophers to adopt an unambiguous terminology in plant cognition. This work explores plant cognition from a philosophical perspective, highlighting its indisputable significance for biosemiotics. Plants are either cognitive agents or not cognitive. Here, I recommend abandoning vague terms like "cognitive-like" when describing plant abilities and behaviors. The first section of this work explores plant cognitive sciences and proposes a discussion around cognition. The second section addresses two issues: interpreting plant cognitive behaviors according to the anthropogenic approach to cognition and the anthropocentric and zoocentric tension in plant cognition. I provide empirical evidence of plants’ cognitive behaviors, such as perception, communication, and decision-making, and discuss critical issues. In the third section, I argue that “cognitive-like” perpetuates an anthropocentric view of plant cognition. This terminology creates a biased framework that hinders a proper understanding of plant behaviors.
... For systems that lack the capacity to report on such states, the attribution of goals is empirically unmoored and arbitrary (see Figure 1). Is it the goal of a given stem cell to differentiate? (Levin, 2021(Levin, , 2022Manicka & Levin 2019) Or, if the stem cell fails to differentiate and dies, was that really its goal? In order for goal-attributions to explain anything, goals would need to be linked to some empirically detectable feature of the system other than the actual outcomes of its behaviour. ...
... One reason given for encouraging the parallel between biological and cognitive goal-directedness is that it appears to aid in understanding how cognitive agency has evolved out of simpler ancestral forms (Levin, 2019;Levin & Dennett 2020). For example, one can avoid positing sharp discontinuities or emergence events by picturing cognitive agency as being on a continuum or gradient (Levin, 2019(Levin, , 2021Seifert et al., 2024;Watson, 2024) stretching back in time, with, say, bacteria also having cognitive states but to a lesser degree (Margulis, 2001;Reber et al., 2023). A "gradualist" view of agency may even seem more consistent with evolutionary gradualism (Levin, 2022). ...
This paper evaluates recent work purporting to show that the “agency” of organisms is an important phenomenon for evolutionary biology to study. Biological agency is understood as the capacity for goal-directed, self-determining activity—a capacity that is present in all organisms irrespective of their complexity and whether or not they have a nervous system. Proponents of the “agency perspective” on biological systems have claimed that agency is not explainable by physiological or developmental mechanisms, or by adaptation via natural selection. We show that this idea is theoretically unsound and unsupported by current biology. There is no empirical evidence that the agency perspective has the potential to advance experimental research in the life sciences. Instead, the phenomena that the agency perspective purports to make sense of are better explained using the well-established idea that complex multiscale feedback mechanisms evolve through natural selection.
... While work on developmental plasticity and extended phenotype do consider factors outside of individual bodies [9][10][11][12][13] , the embryo (and its outer perimeter) is most commonly taken to be the natural, self-contained unit of studies on control mechanisms and the origin of specific anatomies in evolutionary morphology, reproductive toxicology, and developmental genetics. However, many phenomena in biology exhibit scalefree or at least multi-scale dynamics [14][15][16] . Thus, we explored the possibility of relationships between development and external social environments, specifically whether instructive information could also propagate horizontally, enabling embryos to benefit from a kind of "wisdom of the crowd" in their cohort [17][18][19][20] . ...
... We conclude that larger group size confers a significant protective effect against both thioridazine-induced death and craniofacial defects Fig. 1 | Conspecifics help resist teratogenic effects. a Embryos were reared in large (n = 300) or small (n = 100) groups and exposed to the dopaminergic agent thioridazine during N&F stages (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). The concentration of the drug was kept equal at 0.4 μg/mL of media and the amount of media with the drug was scaled according to the group size to match the volume of the drug per embryo. ...
Information for organismal patterning can come from a variety of sources. We investigate the possibility that instructive influences for normal embryonic development are provided not only at the level of cells within the embryo, but also via interactions between embryos. To explore this, we challenge groups of embryos with disruptors of normal development while varying group size. Here, we show that Xenopus laevis embryos are much more sensitive to a diverse set of chemical and molecular-biological perturbations when allowed to develop alone or in small groups, than in large groups. Keeping per-embryo exposure constant, we find that increasing the number of exposed embryos in a cohort increases the rate of survival while incidence of defects decreases. This inter-embryo assistance effect is mediated by short-range diffusible signals and involves the P2 ATP receptor. Our data and computational model emphasize that morphogenesis is a collective phenomenon not only at the level of cells, but also of whole bodies, and that cohort size is a crucial variable in studies of ecotoxicology, teratogenesis, and developmental plasticity.
... shows how already unicellular organisms possess basal levels of cognition and intelligent behaviour [43,44,45,51,52,16,11]. Further perplexity arises when considering consciousness in living organisms. ...
... This latter is the main cause for the lack of interest in cognition and intelligence in minimal living systems. At this very moment we can affirm that intelligence is a property extended across all living taxa, and that we can even talk about minimal consciousness, starting at microbial level [54,43,44,45,51,52,16,11]. The functional requests that make possible the existence of such huge fungal colony are beyond the simple or automated addition of neighbouring cells, but require a level of cooperation and informational communication that make necessary to ask for a mechanism that makes possible all these processes, could it be a form of consciousness? [53]. ...
Fungal organisms can perceive the outer world in a way similar to what animals sense. Does that mean that they have full awareness of their environment and themselves? Is a fungus a conscious entity? In laboratory experiments we found that fungi produce patterns of electrical activity, similar to neurons. There are low and high frequency oscillations and convoys of spike trains. The neural-like electrical activity is yet another manifestation of the fungal intelligence. We discuss fungal cognitive capabilities and intelligence in evolutionary perspective, and question whether fungi are conscious and what does fungal consciousness mean, considering their exhibiting of complex behaviours, a wide spectrum of sensory abilities, learning, memory and decision making. We overview experimental evidences of consciousness found in fungi. Our conclusions allow us to give a positive answer to the important research questions of fungal cognition, intelligence and forms of consciousness.
... This is true not only for multicellular organisms derived from unicellular ancestors but also for eukaryotic cells with multiple organelles arising from bacterial ancestors, and for simpler cells that contain the first chromosomes arising from the union of previously free-living self-replicating molecules (Godfrey-Smith, 2009;Maynard Smith and Szathmáry, 1997;Michod, 2000;Okasha, 2006;West et al., 2015). Moreover, the proper functioning of organismstheir robustness, adaptability and evolvabilitydepends on the continued autonomy of their component parts (Levin, 2019;2021a). Multicellular organisms exhibit multi-scale autonomy, a dynamic interplay of competition and cooperation, and coordinated collective action inherent to their development, function and behaviour, while being a society of cells (Fields and Levin, 2022;Levin, 2019;2022;2023;Sonnenschein and Soto, 1999). ...
... Even though genetically identical, the tissues and cells within a classical organism (body) often compete with each other (Gawne et al., 2020); conversely, cells from distant species cooperate well within chimeric organisms (Nanos and Levin, 2022). In addition, genetic information does not always predict the structure and function of bioelectrically modified organisms (Levin, 2014(Levin, , 2021a or of self-organising synthetic living machines (Blackiston et al., 2021;Kriegman et al., 2020). Likewise, often it is the degree of bioelectrical coupling, not genetic differences, that determines whether cellular optimisation occurs at the singlecell level (cancer) vs. at the organ-level (normal morphogenesis) (Chernet and Levin, 2013). ...
Collective intelligence and individual intelligence are usually considered to be fundamentally different. Individual intelligence is uncontroversial. It occurs in organisms with special neural machinery, evolved by natural selection to enable cognitive and learning functions that serve the fitness benefit of the organism, and then trained through lifetime experience to maximise individual rewards. Whilst the mechanisms of individual intelligence are not fully understood, good models exist for many aspects of individual cognition and learning. Collective intelligence, in contrast, is a much more ambiguous idea. What exactly constitutes collective intelligence is often vague, and the mechanisms that might enable it are frequently domain-specific. These cannot be mechanisms selected specifically for the purpose of collective intelligence because collectives are not (except in special circumstances) evolutionary units, and it is not clear that collectives can learn the way individual intelligences do since they are not a singular locus of rewards and benefits. Here, we use examples from evolution and developmental morphogenesis to argue that these apparent distinctions are not as categorical as they appear. Breaking down such distinctions enables us to borrow from and expand existing models of individual cognition and learning as a framework for collective intelligence, in particular connectionist models familiar in the context of neural networks. We discuss how specific features of these models inform the necessary and sufficient conditions for collective intelligence, and identify current knowledge gaps as opportunities for future research.
... This immediately raises the essentially unanswerable question o how this critical inormation got there to begin with. Directly relating ab initio sel-organization to regulative development orces us to ask, at each step o the process, what systems count as "selves," how the environment o each "sel" is dened, and how, in each case, the exchange o inormation between "sel" and environment is implemented (Levin, 2021). ...
... While any system-environment interaction is inormationally symmetricequal quantities o inormation fow in both directionswhat the two parties do with the inormation they receive may be radically dierent. Symmetric inormation fows, in other words, are consistent with "cognitive light cones" (Levin, 2021(Levin, , 2022 o dierent widths and depths, and hence dierent active inerence capabilities, on the two sides o the system-environment boundary. We turn in §4 to an explicit comparison between regulative development and ab initio sel-organization, ocusing rst on characterizing the environments o each active component as inormation sources, and hence as themselves active agents, at each step in the process. ...
Using the formal framework of the Free Energy Principle, we show how generic thermodynamic requirements on bidirectional information exchange between a system and its environment can generate complexity. This leads to the emergence of hierarchical computational architectures in systems that operate sufficiently far from thermal equilibrium. In this setting, the environment of any system increases its ability to predict system behavior by "engineering" the system towards increased morphological complexity and hence larger-scale, more macroscopic behaviors. When seen in this light, regulative development becomes an environmentally-driven process in which "parts" are assembled to produce a system with predictable behavior. We suggest on this basis that life is thermodynamically favorable and that, when designing artificial living systems, human engineers are acting like a generic "environment".
... The key question is not only the scale-up of a unitary cognitive capacity, but the many-into-one transition: the emergence of minds from collectives (Levin 2019(Levin , 2021b, working in spaces beyond those of their component cells. ...
... All images created by Jeremy Guay of Peregrine Creative, except for the planaria image of panel A, which was created by Alexis Pietak. Used with permission; A,B taken with permission from (Levin 2022) Ginsburg and Jablonka 2021;Gokhale et al. 2021;Kuchling et al. 2020;Levin 2021bLevin , 2022Lyon 2019;Ramstead et al. 2022;Smith-Ferguson and Beekman 2020;Timsit and Grégoire 2021;Watson et al. 2022). ...
Each of us made the remarkable journey from mere matter to mind: starting life as a quiescent oocyte (“just chemistry and physics”), and slowly, gradually, becoming an adult human with complex metacognitive processes, hopes, and dreams. In addition, even though we feel ourselves to be a unified, single Self, distinct from the emergent dynamics of termite mounds and other swarms, the reality is that all intelligence is collective intelligence: each of us consists of a huge number of cells working together to generate a coherent cognitive being with goals, preferences, and memories that belong to the whole and not to its parts. Basal cognition is the quest to understand how Mind scales—how large numbers of competent subunits can work together to become intelligences that expand the scale of their possible goals. Crucially, the remarkable trick of turning homeostatic, cell-level physiological competencies into large-scale behavioral intelligences is not limited to the electrical dynamics of the brain. Evolution was using bioelectric signaling long before neurons and muscles appeared, to solve the problem of creating and repairing complex bodies. In this Perspective, I review the deep symmetry between the intelligence of developmental morphogenesis and that of classical behavior. I describe the highly conserved mechanisms that enable the collective intelligence of cells to implement regulative embryogenesis, regeneration, and cancer suppression. I sketch the story of an evolutionary pivot that repurposed the algorithms and cellular machinery that enable navigation of morphospace into the behavioral navigation of the 3D world which we so readily recognize as intelligence. Understanding the bioelectric dynamics that underlie construction of complex bodies and brains provides an essential path to understanding the natural evolution, and bioengineered design, of diverse intelligences within and beyond the phylogenetic history of Earth.
... Friston et al. [13] characterize interesting systems as "strange particles", whose internal (i.e., cognitive) states are inuenced by their actions only via perceived environmental responses; such systems have to "ask questions" of their environments in order to get answers [19]. Such systems, even bacteria and other basal organisms [20], [21], [22], [23], have multiple ways of observing and acting upon their environments and deploy these resources in context-sensitive ways. In operations-research language, they exhibit situational awareness, i.e., awareness of the context of actions [24], and deploy attention systems to manage the informational, thermodynamic, and metabolic costs of maintaining such awareness [12], [22]. ...
... In operations-research language, they exhibit situational awareness, i.e., awareness of the context of actions [24], and deploy attention systems to manage the informational, thermodynamic, and metabolic costs of maintaining such awareness [12], [22]. Situational awareness is dependent on both short-and long-term memory, or more technically, on the period of time over which precise (Bayesian) beliefs exist, sometimes referred to as the temporal depth or horizon of the GM [20], [21]. Upper limits can, therefore, be placed on behavioral complexity by examining the capacity and control of memory systems from the cellular scale [25] upwards. ...
Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. In this Part I, we introduce the free-energy principle (FEP) and the idea of active inference as Bayesian prediction-error minimization, and show how the control problem arises in active inference systems. We then review classical and quantum formulations of the FEP, with the former being the classical limit of the latter. In the accompanying Part II, we show that when systems are described as executing active inference driven by the FEP, their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implemented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales.
... The lack of potential molecular determinism in β-cells aligns with the growing belief that across multiple levels of biological organization -from cells to organs -genomic information alone does not fully dictate cellular function, let alone cell-to-cell interactions (Ball, 2023). It is time to move beyond misinterpretations of Schrödinger's determinism and embrace causal emergence with agency -for example, the ability of cell collectives to regulate themselves and their environment at every level (Schrödinger, 1967;Levin, 2021). ...
Pancreatic islets are specialized regions within the pancreas composed of endocrine cells responsible for producing, storing and releasing key metabolic hormones, including insulin. Insulin plays a crucial role in regulating cell metabolism. When stimulated, beta cells (also known as β-cells) within the pancreatic islets secrete insulin, which promotes anabolic metabolism and returns specific nutrients – including glucose – to the base level. This function is disrupted in conditions where blood sugar levels are either too high, such as diabetes mellitus, or too low.
... This study not only explores theoretical frameworks but also looks into practical applications, presenting case studies where AI systems display behaviors indicative of self-awareness as well as a proposed methodology for reproducibility. The ethical dimensions of these advancements are critically analyzed by researchers like Levin [10], who discuss the moral responsibilities and societal implications inherent in the development of self-aware machines. Furthermore, scholars such as Marcus and Davis [11] advocate for leveraging insights from cognitive science to enhance the adaptability and flexibility of such systems. ...
This article explores the development of a cognitive sense of self within artificial intelligence (AI), emphasizing the transformative potential of self-awareness in enhancing AI functionalities for sophisticated interactions and autonomous decision-making. Rooted in interdisciplinary approaches that incorporate insights from cognitive science and practical AI applications, the study investigates the mechanisms through which AI can achieve self-recognition, reflection, and continuity of identity—key attributes analogous to human consciousness. This research is pivotal for fields such as healthcare and robotics, where AI systems benefit from personalized interactions and adaptive responses to complex environments. The concept of a self-aware AI involves the ability for systems to recognize themselves as distinct entities within their operational contexts, which could significantly enhance their functionality and decision-making capabilities. Further, the study delves into the ethical dimensions introduced by the advent of self-aware AI, exploring the profound questions concerning the rights of AI entities and the responsibilities of their creators. The development of self-aware AI raises critical issues about the treatment and status of AI systems, prompting the need for comprehensive ethical frameworks to guide their development. Such frameworks are essential for ensuring that the advancement of self-aware AI aligns with societal values and promotes the well-being of all stakeholders involved.
... In addition to theoretical exploration, this research examines practical applications, presenting case studies where AI systems display behaviors consistent with self-awareness. Ethical dimensions are critically analyzed, building on work from Lauscher [12] and Levin [13], who address moral responsibilities and societal implications in developing selfaware AI. ...
The burgeoning field of Artificial Intelligence (AI) increasingly focuses on developing systems capable of self-awareness, merging technological innovation with deep ethical and philosophical considerations. This article explores the cognitive sense of self within AI, examining mechanisms through which AI systems may mirror human-like consciousness and self-perception. Despite significant advances, substantial gaps remain in the understanding and practical implementation of self-aware characteristics in AI, particularly in applying theoretical models and ethical frameworks to real-world scenarios. There is a pressing need for comprehensive research to explore these theoretical underpinnings and translate them into operational systems capable of ethical and adaptable behaviors. This study aims to synthesize existing knowledge, identify critical gaps in the literature, and highlight the implications of these findings for the future development of machine learning systems. Integrating insights from cognitive science, neuroscience, and ethical studies, this article seeks to provide a foundational framework for advancing emergent technologies that are both technologically robust and aligned with societal values. The significance of this research lies in its potential to guide the development of machine systems capable of complex decision-making and interactions, addressing both the moral and practical challenges of integrating such systems into daily human activities.
... Planarians (flatworms), which can regenerate their complete body from minute tissue fragments, and the Hydra, which can restore their entire head after amputation of a tentacle are two examples of organisms with exceptional regeneration abilities (Fuchs, 2015). More known creatures, such as salamanders and insects also been shown to have extraordinary limb-regeneration capabilities (Levin, 2021). Most animals have restricted their regeneration but the basic ability to regenerate of gut or skin's epithelial lining, the capability to restore muscle fibers following damage, and the ability to continually replenish blood cells have remained (Elchaninov et al., 2021). ...
... Second, certain cells exhibit the capacity to transform into multicellular entities with novel functionalities postmortem when provided with essential nutrients, oxygen, endogenous bioelectricity, or biochemical cues [7]. Although the organism is technically dead, the cells attain a new life form without changes to their genetic/genomic background. ...
Organismal death has long been considered the irreversible ending of an organism's integrated functioning as a whole. However, the persistence of functionality in organs, tissues, and cells postmortem, as seen in organ donation, raises questions about the mechanisms underlying this resilience. Recent research reveals that various factors, such as environmental conditions, metabolic activity, and inherent survival mechanisms, influence postmortem cellular functionality and transformation. These findings challenge our understanding of life and death, highlighting the potential for certain cells to grow and form new multicellular entities. This opens new avenues in biology and medicine, expanding our comprehension of life's complexity.
... Because robots are not self-aware of their own existence, they do not feel to be mortal. Even with the recent advances of biotechnology, like biobots, the dilemmas of life and death still do not apply to them (Levin, 2021). This idea is in itself quite important because it limits the possibilities of robots as entities. ...
In this paper we discuss the possibility of robots having a mind and being able to act like human beings and even surpass the human intelligence, and in consequence taking over the world. It is possibility that has been put forward in human history long ago, and that has been accentuated with the new advances in technology from the last few years, of which Chat GPT is the last very well-known example. We base ourselves in a literature review made on eight basic features we define as characteristic of humans, namely: Reproduction, Creation, Belonging, Citizenship, Self-Awareness, Mortality, Rationality, Humour, Feelings and Emotions. We use a plurality of databases as Google and SCOPUS. As a result, we conclude that even if robots may express themselves as humans, and may beat humans in specific activities, they lack most of the features that define human beings and most probably they will ever do. As with time and space travelling, robots that would take power on Earth are a utopia that will probably never happen, but whose pursue will be beneficial for the human race. The paper has the limitation of being only theoretical, and the originality of being based on Philosophy of Artificial Intelligence and presented in a scientific environment.
... By expanding our knowledge and appreciation of cell physiology, we unlock the potential for transformative advancements that will shape the future of medicine and ultimately benefit all of humanity. [16,17,18,19,20,21,22,23,24] ...
... The subject with his or her self is central to the question of life or death (Levin, 2021). This is how the ontological and anthropological dimensions in medical ethics are synchronised. ...
The purpose of the article is to highlight the key philosophical cases in the medical and ethical debate on life and death - ontological, axiological and anthropological. For the philosophy of medicine, the concepts of life and health are fundamental dimensions, as they combine elements of nature and human existence. The aim of the article is to differentiate the existential, value and human contexts of philosophy in the medical space. The methodology used in the study is focused mainly on the analytical cluster of general scientific knowledge. The analysis of the literature on the problem of life and death in medical and ethical discourse allowed us to separately group the medical, philosophical and scientific and worldview clusters of this problem. Through generalising and comparative analysis, an attempt is made to unify the problem of life and death in the context of a single philosophical and scientific paradigm. To achieve these objectives, it is advisable to use the principles of interdisciplinarity, which help to bring to a common understanding the various ideas and views on the ethical component of the dichotomy of life and death in medicine. The results of the study indicate that the human-dimensional philosophical component dominates the ontological and axiological components in the modern worldview of human existence. This is the result of the policy of anthropocentrism in its global manifestation and the consequence of the use of a pragmatic approach in the system of human sciences. However, the COVID-19 pandemic, a minor factor by civilisational standards, has managed (albeit for a short period) to reorient the philosophical issues of life and death to existential dimensions. A promising area of research is modelling the situation of a socio-cultural crisis of a global scale in the healthcare system and the readiness of society to re-position the problem of life and death. Thus, the philosophy of medicine clearly structures the problem of life and death in three fundamental cases: ontological, axiological and anthropological, which change their priority in the scientific and philosophical discourse depending on the socio-cultural trends in the development of society and civilisation.
... Practical ability to build a systematic approach to the processing of available medical information, and to make decisions based on the mastered theoretical and practical developments of external influences on the body is necessary for proper diagnosis and determination of the necessary degree of medical influence to resolve a specific situation of medical practice [13]. This determines the importance of a highquality combination of teaching medical biophysics with related natural science disciplines to form high-quality competencies of future specialists in the medical field, necessary for them to carry out their professional activities in the future. ...
Relevance. The relevance of the study is conditioned by the need to form clear principles of teaching medical biophysics to students, considering the main features of teaching biology and related disciplines in higher educational institutions of Kazakhstan at the moment. Purpose. The purpose of this study is to investigate the basic principles of teaching medical biophysics in profession-oriented areas in the system of higher educational institutions of the Republic of Kazakhstan, to identify similar teaching trends and form an assessment of the overall effectiveness of teaching this discipline in the system of educational institutions under consideration. Methodology. The basis of the methodological approach in this study is a combination of a systematic analysis of the methodological foundations of combining the principles of teaching biology and physics in a modern higher educational institution, with an analytical investigation of the main aspects of teaching medical biophysics as a major subject of a number of modern higher educational institutions. Results. The results obtained are a clear demonstration of the importance of the qualitative study of medical biophysics in higher educational institutions of Kazakhstan, to develop students' competencies necessary for their subsequent professional activities. Conclusions. The findings and the conclusions formulated on their basis are of significant importance for students of medical departments of universities of Kazakhstan studying medical biophysics as a principal subject of the general training programme, and representatives of the teaching staff of these educational institutions, who, by the nature of their professional activities, are faced with the need to search for and practical implementation of effective principles of teaching this subject within the requirements of the university curriculum.
... While these assumptions are today frequently rejected (West-Eberhard [2003]; Gilbert and Epel [2009]), many research programmes in developmental biology (for instance, Davidson et al. [2002]; Istrail et al. [2007]) and related fields (such as systems and synthetic biology; see Davidson [2006]; Alon [2007]; Danchin [2009]; Venter [2014]; Bassel [2018]; Bongard and Levin [2021]) continue to promote computational explanations of biological systems. A possible rationale in favour of this view is that, by knowing their computational features, we would be able to manipulate and reconfigure biological systems (Venter [2014]; Kriegman et al. [2020]; Blackinston et al. [2021]), perform more refined experiments (Istrail [2019]; Bongard and Levin [2023]), or control undesired developmental processes, such as cancer (Levin [2021]; Manicka and Levin This is the author's acceptod manuscript without copyediting, formatting, or final corrections. It will be published in its final form in an upcoming issue of The British Joumal for the Philosophy of Science, published by The University of Chicago Press on behalf of The British Society for the Philosophy of Science. ...
Although the analogy between genome-based developmental program and computer software has been extensively criticised, many research programmes in developmental biology and related areas still champion computational explanations of biological systems. To clarify what kinds of ontological assumptions might be shared by these explanations and assess whether they are warranted, we adopt Piccinini’s mechanistic account of concrete computation. According to this account, a computing system is a functional mechanism performing operations on physical entities, called vehicles, following rules that are solely sensitive to differences between their spatiotemporal parts, called portions. We articulate the mechanistic requirements for being a computing system and critically evaluate whether biological systems satisfy them.
... The most inclusive definition of intentionality for animacy recognizes it as the ability to perceive and act upon the environment (Dahl, 2008). Living systems and creatures use molecules with memory (i.e., DNA) and are a mixture of chemicals and responses at some level or another (Levin, 2021). This definition and concept of intentionality is mixed with many other important terms and frameworks such as autonomy, agency, and enactivism. ...
Animacy is an important framework through which humans view and categorize the world, but many objects do not easily fit within this scale. Plants are unique because they are very familiar to humans, yet the features and traits relevant for placement within the animacy scale are generally poorly understood by the public. Animacy occurs at three levels, with the inherent attributes of the object (biology), how they are perceived (cognition), and how they are expressed in languages (linguistics). Animacy is dependent on qualification and perception as alive, mobile, and intentional. In the absence of visible movement, classification is dependent on featural attributes indicating mobility or placement in a group recognized as animate (animalness). Plants have complicated bodies whose forms and structures are frequently clear representations of their life history and function (plantness), more than many animals, yet these signs of movement and activity are rarely recognized. The animacy scale may be more closely based on human similarity (humanness) with humans as the peak of life, mobility, and intentionality. As humans, we can have an anthropocentric viewpoint, rendering plants as scenery or utility, or use anthropomorphic interaction to better understand and recognize the dynamic lives of plants. The goal of this review is to compare the current evidence on the placement of plants within animacy and adjacent scales with the biology and habit of land plants, to better understand human perception and behaviour, and work with this process to educate and inform people about the complex lives of plants.
... This position suggests that intelligent problem-solving competencies are not a special case in biological systems, but rather a fundamental feature of all organisms-it is precisely what makes an organism more than the sum of its parts, and an active agent in constructing itself (Thompson 2010;Levin 2021bLevin , 2022Levin et al. 2021;Lyon et al. 2021;Watson et al. 2022;Watson and Levin 2023). ...
In this essay we aim to present some considerations regarding a minimal but concrete notion of agency and goal-directed behavior that are useful for characterizing biological systems at different scales. These considerations are a particular perspective, bringing together concepts from dynamical systems, combinatorial problem-solving, and connectionist learning with an emphasis on the relationship between parts and wholes. This perspective affords some ways to think about agents that are concrete and quantifiable, and relevant to some important biological issues. Instead of advocating for a strict definition of minimally agential characteristics, we focus on how (even for a modest notion of agency) the agency of a system can be more than the sum of the agency of its parts. We quantify this in terms of the problem-solving competency of a system with respect to resolution of the frustrations between its parts. This requires goal-directed behavior in the sense of delayed gratification, i.e., taking dynamical trajectories that forego short-term gains (or sustain short-term stress or frustration) in favor of long-term gains. In order for this competency to belong to the system (rather than to its parts or given by its construction or design), it can involve distributed systemic knowledge that is acquired through experience, i.e., changes in the organization of the relationships among its parts (without presupposing a system-level reward function for such changes). This conception of agency helps us think about the ways in which cells, organisms, and perhaps other biological scales, can be agential (i.e., more agential than their parts) in a quantifiable sense, without denying that the behavior of the whole depends on the behaviors of the parts in their current organization.
... One of the other anxieties experienced by students is unpreparedness (Kumar et al., 2021) when faced with practicum activities in the laboratory (Irwanto & Farhanto, 2021). This situation occurs because students are not given enough scientific inquiry-based lessons such as evaluating and designing skills in scientific inquiry so that students tend to be passive and Biology subjects are often considered as lessons that only focus on theory and memorization which are quite difficult (Levin, 2021;Sumarra et al., 2020). ...
After over two years of the COVID-19 pandemic, learning policies demands adjustments from teachers and students, especially in practicum and scientific inquiry. To better prepare students for practicum, creating pre-laboratory journals is one approach. This study aimed to assess how pre-laboratory journals impact the development of scientific inquiry skills and socio-emotional growth in high school students. It used a quasi-experimental design with pretest-posttest control groups and selected participants through cluster random sampling. This study involved 70 students of 11th grade students consisting of 35 students of control class and 35 students of experimental class at Senior High School 1 Cisarua, West Bandung. The instrument used include five test that assessed and designed scientific inquiry skills related to the human excretory system, as part of the scientific literacy framework. The results revealed significant differences in the scientific inquiry skills between high school students in the experimental and control classes (p-value 0.00 0.05). The control class showed a moderate average improvement (N-gain of 0.53), while the experimental class exhibited a high average improvement (N-gain of 0.71). the control group was more anxious than the experimental group. Therefore, using pre-lab journals significantly improved both high school students' scientific skills and emotional well-being.
... Synthetic biology may provide a way to gain some insight and to test specific hypotheses. If technology advances sufficiently, it may be possible to create various types of biochemical networks that have random properties with respect to specific adaptive functions [22]. One could then use experimental evolution to analyze the conditions under which cells can improve their ability to read the information in the random biochemical reservoir to achieve those specific functions. ...
Organisms perceive their environment and respond. The origin of perception-response traits presents a puzzle. Perception provides no value without response. Response requires perception. Recent advances in machine learning may provide a solution. A randomly connected network creates a reservoir of perceptive information about the recent history of environmental states. In each time step, a relatively small number of inputs drives the dynamics of the relatively large network. Over time, the internal network states retain a memory of past inputs. To achieve a functional response to past states or to predict future states, a system must learn only how to match states of the reservoir to the target response. In the same way, a random biochemical or neural network of an organism can provide an initial perceptive basis. With a solution for one side of the two-step perception-response challenge, evolving an adaptive response may not be so difficult. Two broader themes emerge. First, organisms may often achieve precise traits from sloppy components. Second, evolutionary puzzles often follow the same outlines as the challenges of machine learning. In each case, the basic problem is how to learn, either by artificial computational methods or by natural selection.
... The opportunity is to borrow from nature, not the specific genes and pathways comprising today's developmental biology textbooks, but the ways in which non-neural systems solve problems in diverse problem spaces by scaling their basal homeostatic properties into larger goals. 89,90 The implications for AI and behavioral science in general concern biobots' degrees of proto-cognitive sophistication. No claims have yet been made for the degree of agency they can exhibit (this characterization is currently under way), but it is clear that humans' intuitions about recognizing intelligent behavior are highly limited by the training set of familiar animals behaving in 3D space. ...
Advances in science and engineering often reveal the limitations of classical approaches initially used to understand, predict, and control phenomena. With progress, conceptual categories must often be re-evaluated to better track recently discovered invariants across disciplines. It is essential to refine frameworks and resolve conflicting boundaries between disciplines such that they better facilitate, not restrict, experimental approaches and capabilities. In this essay, we address specific questions and critiques which have arisen in response to our research program, which lies at the intersection of developmental biology, computer science, and robotics. In the context of biological machines and robots, we explore changes across concepts and previously distinct fields that are driven by recent advances in materials, information, and life sciences. Herein, each author provides their own perspective on the subject, framed by their own disciplinary training. We argue that as with computation, certain aspects of developmental biology and robotics are not tied to specific materials; rather, the consilience of these fields can help to shed light on issues of multiscale control, self-assembly, and relationships between form and function. We hope new fields can emerge as boundaries arising from technological limitations are overcome, furthering practical applications from regenerative medicine to useful synthetic living machines.
... And structure and function are both highly plastic. Cognition continues to function despite important changes to the body/brain and the corresponding modification of its information at the cellular, molecular, or bioelectric level [22]. ...
Complex living agents consist of cells, which are themselves competent sub-agents navigating physiological and metabolic spaces. Behaviour science, evolutionary developmental biology and the field of machine intelligence all seek to understand the scaling of biological cognition: what enables individual cells to integrate their activities to result in the emergence of a novel, higher-level intelligence with large-scale goals and competencies that belong to it and not to its parts? Here, we report the results of simulations based on the TAME framework, which proposes that evolution pivoted the collective intelligence of cells during morphogenesis of the body into traditional behavioural intelligence by scaling up homeostatic competencies of cells in metabolic space. In this article, we created a minimal in silico system (two-dimensional neural cellular automata) and tested the hypothesis that evolutionary dynamics are sufficient for low-level setpoints of metabolic homeostasis in individual cells to scale up to tissue-level emergent behaviour. Our system showed the evolution of the much more complex setpoints of cell collectives (tissues) that solve a problem in morphospace: the organization of a body-wide positional information axis (the classic French flag problem in developmental biology). We found that these emergent morphogenetic agents exhibit a number of predicted features, including the use of stress propagation dynamics to achieve the target morphology as well as the ability to recover from perturbation (robustness) and long-term stability (even though neither of these was directly selected for). Moreover, we observed an unexpected behaviour of sudden remodelling long after the system stabilizes. We tested this prediction in a biological system—regenerating planaria—and observed a very similar phenomenon. We propose that this system is a first step towards a quantitative understanding of how evolution scales minimal goal-directed behaviour (homeostatic loops) into higher-level problem-solving agents in morphogenetic and other spaces.
We argue here that the Origin of Life (OOL) problem is not just a chemistry problem but is also, and primarily, a cognitive science problem. When interpreted through the lens of the Conway-Kochen theorem and the Free Energy Principle, contemporary physics characterizes all complex dynamical systems that persist through time as Bayesian agents. If all persistent systems are to some - perhaps only minimal - extent cognitive, are all persistent systems to some extent alive, or are living systems only a subset of cognitive systems? We argue that no bright line can be drawn, and we re-assess, from this perspective, the Fermi paradox and the Drake equation. We conclude that improving our abilities to recognize and communicate with diverse intelligences in diverse embodiments, whether based on familiar biochemistry or not, will either resolve or obviate the OOL problem.
The Diverse Intelligence research seeks to understand commonalities in behavioral competencies across a wide range of implementations. Especially interesting are simple systems that provide unexpected examples of memory, decision-making, or problem-solving in substrates that at first glance do not appear to be complex enough to implement such capabilities. We seek to develop tools to determine minimal requirements for such capabilities, and to learn to recognize and predict basal forms of intelligence in unconventional substrates. Here, we apply novel analyses to the behavior of classical sorting algorithms—short pieces of code studied for many decades. To study these sorting algorithms as a model of biological morphogenesis and its competencies, we break two formerly ubiquitous assumptions: top-down control (instead, each element within an array of numbers can exert minimal agency and implement sorting policies from the bottom up), and fully reliable hardware (instead, allowing elements to be “damaged” and fail to execute the algorithm). We quantitatively characterize sorting activity as traversal of a problem space, showing that arrays of autonomous elements sort themselves more reliably and robustly than traditional implementations in the presence of errors. Moreover, we find the ability to temporarily reduce progress in order to navigate around a defect, and unexpected clustering behavior among elements in chimeric arrays consisting of two different algorithms. The discovery of emergent problem-solving capacities in simple, familiar algorithms contributes a new perspective showing how basal forms of intelligence can emerge in simple systems without being explicitly encoded in their underlying mechanics.
This paper examines body-related metaphors used by Polish women to describe lived experiences associated with Turner syndrome (TS), and highlights the contribution this form of analysis can make to the study of health, emotional well-being, and social identity. Turner syndrome is a genetic aberration that affects females, and results in short stature, ovarian failure and a number of less typical body deformations; it often takes a long time to be appropriately diagnosed. Metaphor analysis is employed to analyze a data subset of four semi structured interviews audio recorded and translated from Polish into English. The analysis is carried out with metaphor operationalized as a framing device in discourse, whose main function is to impose a particular axiologically-charged construal of TS. Metaphorical concepts lying at the basis of the metaphors used were identified and grouped into four themes: (i) diagnosis and therapy; (ii) Turner syndrome (iii) appearance (iv) self-esteem and social positioning. The results of the analysis show that a range of composite metaphors develop on the basis of the BODY IS A PHYSICAL OBJECT as a primary metaphor but their occurrence depends on the salience of particular bodily symptoms of TS in individual women. Results are discussed with regard to the function and the utility of metaphor analysis in health, emotional well-being, and social identity research.
Multiple theoretical models of dissociative experiences have been formulated over the last century. These theories are clinically useful; however, it remains unclear if common factors exist in various pathways leading to an onset of dissociations. This article is the first in a series of papers, where we provide a framework for building an integrated model of dissociative experiences. This framework combines a first-principles-based perspective with dynamical systems perspective. We propose that temporal depth collapse can be a possible common factor in dissociative episodes of any etiology, moreover, we consider this factor to have causal power. In a follow-up paper, we will examine this model from the standpoint of clinical practice and neurobiological theories of dissociations. We will also provide empirical data in support of this model and present ideas for therapeutic applications.
Memory, the cornerstone of adaptive behavior, has been extensively studied within the confines of neural systems. A comprehensive literature review was conducted using key academic databases such as PubMed, Web of Science, and Google Scholar. The methodology prioritized peer-reviewed papers, reviews, and primary research, with a particular emphasis on articles relevant to chemical memory in primitive organisms. This review sheds light on the less-explored territory of chemical memory in primitive organisms. From the complex neural pathways in humans to the basic chemical interactions in bacteria and protozoa, this study underscores the evolutionary significance and ubiquity of information storage in biology. A notable highlight is the exploration of the ability of plants to adaptively respond to their environment despite lacking a central nervous system, emphasizing the role of epigenetic markers in stress memory. In organisms devoid of advanced neural systems, chemical reactions, and interactions pave the way for memory mechanisms, offering insight into the foundational building blocks of memory. The findings presented challenges in traditional neuro-centric views, broadening our understanding of memory and highlighting its pivotal role in the evolutionary tapestry of life.
Biological organisms exhibit phenomenal adaptation through morphology‐shifting mechanisms including self‐amputation, regeneration, and collective behavior. For example, reptiles, crustaceans, and insects amputate their own appendages in response to threats. Temporary fusion between individuals enables collective behaviors, such as in ants that temporarily fuse to build bridges. The concept of morphological editing often involves the addition and subtraction of mass and can be linked to modular robotics, wherein synthetic body morphology may be revised by rearranging parts. This work describes a reversible cohesive interface made of thermoplastic elastomer that allows for strong attachment and easy detachment of distributed soft robot modules without direct human handling. The reversible joint boasts a modulus similar to materials commonly used in soft robotics, and can thus be distributed throughout soft robot bodies without introducing mechanical incongruities. To demonstrate utility, the reversible joint is implemented in two embodiments: a soft quadruped robot that self‐amputates a limb when stuck, and a cluster of three soft‐crawling robots that fuse to cross a land gap. This work points toward future robots capable of radical shape‐shifting via changes in mass through autotomy and interfusion, as well as highlights the crucial role that interfacial stiffness change plays in autotomizable biological and artificial systems.
Xenobot, the world’s first biological robot, puts numerous philosophical riddles before us. One among them pertains to the cognitive status of these entities. Are these biological robots cognitive? To evaluate the cognitive status of xenobots and to resolve the puzzle of a single mind emerging from smaller sub-units, in this article, I juxtapose the cognitive capacities of xenobots with that of two other minimal models of cognition, i.e., basal cognition and nonliving active matter cognition. Further, the article underlines the essential cognitive capabilities that xenobots need to achieve to enter what I call stage 1 of xenobotic cognition. Stage 1 is characterized by numerous cognitive mechanisms, which are integral for the survival and cognition of basal organisms. Finally, I suggest that developing xenobots that can reach Stage 1 can help us achieve sophistication in the areas of evolution of the human mind, robotics, biology and medicine, and artificial intelligence (AI).
The immune system is a central component of organismic function in humans. This paper addresses self‐organization of biological systems in relation to—and nested within—other biological systems in pregnancy. Pregnancy constitutes a fundamental state for human embodiment and a key step in the evolution and conservation of our species. While not all humans can be pregnant, our initial state of emerging and growing within another person's body is universal. Hence, the pregnant state does not concern some individuals but all individuals. Indeed, the hierarchical relationship in pregnancy reflects an even earlier autopoietic process in the embryo by which the number of individuals in a single blastoderm is dynamically determined by cell– interactions. The relationship and the interactions between the two self‐organizing systems during pregnancy may play a pivotal role in understanding the nature of biological self‐organization per se in humans. Specifically, we consider the role of the immune system in biological self‐ organization in addition to neural/brain systems that furnish us with a sense of self. We examine the complex case of pregnancy, whereby two immune systems need to negotiate the exchange of resources and information in order to maintain viable self‐regulation of nested systems. We conclude with a proposal for the mechanisms—that scaffold the complex relationship between two self‐organising systems in pregnancy—through the lens of the Active Inference, with a focus on shared Markov blankets.
The idea of applying cognitive kind terms and concepts to ‘unconventional’ systems has gained steam. Perhaps unsurprisingly, this idea also has been met with skepticism. There is an implicit worry amongst skeptics that the idea of applying cognitive kind terms and concepts to non-humans, or at least to non-humans that are anatomically quite unlike humans, amounts to a Mere Honorific Conclusion: to say that a system is cognitive is to say it is merely worthy of investigation. In this paper, I use this conclusion as a framing device for exploring how we ought to approach the idea of cognition in unconventional systems, and I explore two avenues for blocking it: unification and generativity.
Recent proposals that the substrate of memory is molecular raise questions about where this molecular model stands in relation to the dominant synaptic model of memory. In this paper, we address the perceived rivalry between these models and ask whether they can be integrated. We argue that addressing rivalry or integration requires delineating the explananda of synaptic and molecular models, as well as revisiting assumptions about how these models account for their explananda. The perceived rivalry between these models exemplifies epistemic costs that arise when we try to pit explanatory models as rivals or integrate them.
Significant efforts have been made in the past decades to understand how mental and cognitive processes are underpinned by neural mechanisms in the brain. This paper argues that a promising way forward in understanding the nature of human cognition is to zoom out from the prevailing picture focusing on its neural basis. It considers instead how neurons work in tandem with other type of cells (e.g., immune) to subserve biological self-organization and adaptive behavior of the human organism as a whole. We focus specifically on the immune cellular processing as key actor in complementing neuronal processing in achieving successful self-organization and adaptation of the human body in an ever-changing environment. We overview theoretical work and empirical evidence on "basal cognition" challenging the idea that only the neuronal cells in the brain have the exclusive ability to "learn" or "cognize." The focus on cellular rather than neural, brain processing underscores the idea that flexible responses to fluctuations in the environment require a carefully crafted orchestration of multiple cellular and bodily systems at multiple organizational levels of the biological organism. Hence cognition can be seen as a multiscale web of dynamic information processing distributed across a vast array of complex cellular (e.g., neuronal, immune, and others) and network systems, operating across the entire body, and not just in the brain. Ultimately, this paper builds up toward the radical claim that cognition should not be confined to one system alone, namely, the neural system in the brain, no matter how sophisticated the latter notoriously is.
In 2021 I noted that in all information-based systems we understand, Cognition creates Code, which controls Chemical reactions. Known agents write software which controls hardware, and not the other way around. I proposed the same is true in all of biology. Though the textbook description of cause and effect in biology proposes the reverse, that Chemical reactions produce Code from which Cognition emerges, there are no examples in the literature demonstrating either step. A mathematical proof for the first step, cognition generating code, is based on Turing's halting problem. The second step, code controlling chemical reactions, is the role of the genetic code. Thus a central question in biology: What is the nature and source of cognition? In this paper I propose a relationship between biology and Quantum Mechanics (QM), hypothesizing that the same principle that enables an observer to collapse a wave function also grants biology its agency: the organism's ability to act on the world instead of merely being a passive recipient. Just as all living cells are cognitive (Shapiro 2021, 2007; McClintock 1984; Lyon 2015; Levin 2019, Pascal and Pross, 2022), I propose humans are quantum observers because we are made of cells and all cells are observers. This supports the century-old view that in QM, the observer does not merely record the event but plays a fundamental role in its outcome.The classical world is driven by laws, which are deductive; the quantum world is driven by choices, which are inductive. When the two are combined, they form the master feedback loop of perception and action for all biology. In this paper I apply basic definitions of induction, deduction and computation to known properties of QM to show that the organism altering itself (and its environment) is a whole shaping its parts. It is not merely parts comprising a whole. I propose that an observer collapsing the wave function is the physical mechanism for producing negentropy. The way forward in solving the information problem in biology is understanding the relationship between cognition and QM.
Using the concept of "plasticity", or the brain’s ability to change through growth and reorganization, as a theoretical framework, this book argues that encouraging an exploration of the self better establishes emotional value in the composition classroom.
This book explores recent evidence from studies in modern neuroscience to provide biological correlations between current and developing theory and pedagogy in Composition Studies. Starting with the concept of self, each subsequent chapter builds a neurobiological understanding of how emotional value, intrinsic motivation, creativity and happiness are constructed and felt. This material exploration shows how these factors can maintain motivation, improve long-term memory, encourage creative risk, and initiate complex considerations of being. Recognizing the shift in Composition Studies to posthuman and new materialist methodologies, this modern neuroscience is presented as a useful parallel to—rather than being at odds with—these and other current methodologies, theories, and pedagogies.
Outlining the need for a more student-focused, guided-discovery framework for the composition classroom, this interdisciplinary resource will be of interest to scholars and students in the field of Composition Studies, Communication Studies, Education, Psychology and Philosophy.
Myxobacteria and dictyostelids are prokaryotic and eukaryotic multicellular lineages, respectively, that after nutrient depletion aggregate and develop into structures called fruiting bodies. The developmental processes and resulting morphological outcomes resemble one another to a remarkable extent despite their independent origins, the evolutionary distance between them and the lack of traceable homology in molecular mechanisms. We hypothesize that the morphological parallelism between the two lineages arises as the consequence of the interplay within multicellular aggregates between generic processes, physical and physicochemical processes operating similarly in living and non-living matter at the mesoscale (~10-3-10-1 m) and agent-like behaviors, unique to living systems and characteristic of the constituent cells, considered as autonomous entities acting according to internal rules in a shared environment. Here, we analyze the contributions of generic and agent-like determinants in myxobacteria and dictyostelid development and their roles in the generation of their common traits. Consequent to aggregation, collective cell-cell contacts mediate the emergence of liquid-like properties, making nascent multicellular masses subject to novel patterning and morphogenetic processes. In both lineages, this leads to behaviors such as streaming, rippling, and rounding-up, as seen in non-living fluids. Later the aggregates solidify, leading them to exhibit additional generic properties and motifs. Computational models suggest that the morphological phenotypes of the multicel-lular masses deviate from the predictions of generic physics due to the contribution of agent-like behaviors of cells such as directed migration, quiescence, and oscillatory signal transduction mediated by responses to external cues. These employ signaling mechanisms that reflect the evolutionary histories of the respective organisms. We propose that the similar developmental trajectories of myxobacteria and dictyostelids are more due to shared generic physical processes in coordination with analogous agent-type behaviors than to convergent evolution under parallel selection regimes. Insights from the biology of these aggregative forms may enable a unified understanding of developmental evolution, including that of animals and plants.
With Hebbian learning ‘who fires together wires together’, well-known problems arise. Hebbian plasticity can cause unstable network dynamics and overwrite stored memories. Unstable dynamics can partly be addressed with homeostatic plasticity mechanisms. Unfortunately, the time constants of homeostatic mechanisms required in network models are much shorter than those measured experimentally.
We propose that homeostatic time constants can be slow if plasticity is gated. We investigate how gating plasticity influences network stability and memories in plastic balanced spiking networks of neurons with dendrites. We compare how different factors such as excitability, learning rate, and inhibition lift the requirements for homeostatic time constants. We investigate how dendritic versus perisomatic gating allows for different amounts of weight changes in stable networks. We suggest that the compartmentalisation of pyramidal cells enables dendritic synaptic changes while maintaining stability. We show that spatially restricted plasticity improves stability. Finally, we compare how different gates protect memories.
Significance statement
How does the brain maintain stable neural activity in the presence of synaptic changes? This question has been studied extensively in the past, but we argue that one crucial aspect is missing in previous studies. While all theoretical work has assumed plasticity to be on all the time, plasticity is in fact heavily gated. In this light, we must reconsider the theories on stability and homeostasis of neural activity. In particular, theoretical studies show that neural networks undergoing plasticity require fast compensatory homeostatic mechanisms to be stable. However, experimentally measured homeostatic processes operate on much slower time scales. We studied how the gating of plasticity can improve network stability and thereby reduce the discrepancy in the homeostatic time constant between models and experiments.
Purpose:
To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning.
Methods:
A systematic literature search in PubMed was performed for articles pertinent to the topic of artificial intelligence methods used in medicine with an emphasis on ophthalmology.
Results:
A review of machine learning and deep learning methodology for the audience without an extensive technical computer programming background.
Conclusions:
Artificial intelligence has a promising future in medicine; however, many challenges remain.
Translational relevance:
The aim of this review article is to provide the nontechnical readers a layman's explanation of the machine learning methods being used in medicine today. The goal is to provide the reader a better understanding of the potential and challenges of artificial intelligence within the field of medicine.
It remains at best controversial to claim, non-figuratively, that plants are cognitive agents. At the same time, it is taken as trivially true that many (if not all) animals are cognitive agents, arguably through an implicit or explicit appeal to natural science. Yet, any given definition of cognition implicates at least some further processes, such as perception, action, memory, and learning, which must be observed either behaviorally, psychologically, neuronally, or otherwise physiologically. Crucially, however, for such observations to be intelligible, they must be counted as evidence for some model. These models in turn point to homologies of physiology and behavior that facilitate the attribution of cognition to some non-human animals. But, if one is dealing with a model of animal cognition, it is tautological that only animals can provide evidence, and absurd to claim that plants can. The more substantive claim that, given a general model of cognition, only animals but not plants can provide evidence, must be evaluated on its merits. As evidence mounts that plants meet established criteria of cognition, from physiology to behavior, they continue to be denied entry into the cognitive club. We trace this exclusionary tendency back to Aristotle, and attempt to counter it by drawing on the philosophy of modelling and a range of findings from plant science. Our argument illustrates how a difference in degree between plant and animals is typically mistaken for a difference in kind.
Orthotopic liver transplantation continues to be the only effective therapy for patients with end‐stage liver disease. Unfortunately, many of these patients are not considered transplant candidates, lacking effective therapeutic options that would address both the irreversible progression of their hepatic failure and the control of their portal hypertension. In this prospective study, a swine model was exploited to induce sub‐acute liver failure. Autologous hepatocytes, isolated from the left hepatic lobe, were transplanted into the mesenteric lymph nodes by direct cell injection. 30 to 60 days after transplantation, hepatocyte engraftment in lymph nodes was successfully identified in all transplanted animals with the degree of ectopic liver mass detected being proportional to the induced native liver injury. These ectopic livers developed within the lymph nodes showed remarkable histologic features of swine hepatic lobules, including the formation of sinusoids and bile ducts. Based on our previous tyrosinemic mouse model and the present pig models of induced sub‐acute liver failure, the generation of auxiliary liver tissue using the lymph nodes as hepatocyte engraftment sites represents a potential therapeutic approach to supplement declining hepatic function in the treatment of liver disease.
Planarians exhibit traits of cephalization but are unique among bilaterians in that they ingest food by means of goal-directed movements of a trunk-positioned pharynx, following protrusion of the pharynx out of the body, raising the question of how planarians control such a complex set of body movements for achieving robust feeding. Here, we use the freshwater planarian Dugesia japonica to show that an isolated pharynx amputated from the planarian body self-directedly executes its entire sequence of feeding functions: food sensing, approach, decisions about ingestion, and intake. Gene-specific silencing experiments by RNA interference demonstrated that the pharyngeal nervous system (PhNS) is required not only for feeding functions of the pharynx itself but also for food-localization movements of individual animals, presumably via communication with the brain. These findings reveal an unexpected central role of the PhNS in the linkage between unique morphological phenotypes and feeding behavior in planarians.
Electrical signalling in biology is typically associated with action potentials—transient spikes in membrane voltage that return to baseline. Hodgkin–Huxley and related conductance-based models of electrophysiology belong to a more general class of reaction–diffusion equations that could, in principle, support the spontaneous emergence of patterns of membrane voltage that are stable in time but structured in space. Here, we show theoretically and experimentally that homogeneous or nearly homogeneous tissues can undergo spontaneous spatial symmetry breaking through a purely electrophysiological mechanism, leading to the formation of domains with different resting potentials separated by stable bioelectrical domain walls. Transitions from one resting potential to another can occur through long-range migration of these domain walls. We map bioelectrical domain wall motion using all-optical electrophysiology in an engineered cell line and in human induced pluripotent stem cell (iPSC)-derived myoblasts. Bioelectrical domain wall migration may occur during embryonic development and during physiological signalling processes in polarized tissues. These results demonstrate that nominally homogeneous tissues can undergo spontaneous bioelectrical spatial symmetry breaking. A detailed theoretical and experimental investigation of homogeneous cell tissues finds that they can undergo spontaneous spatial symmetry breaking through a purely electrophysiological mechanism.
Habituation, defined as the reversible decrement of a response during repetitive stimulation, is widely established as a form of non-associative learning. Though more commonly ascribed to neural cells and systems, habituation has also been observed in single aneural cells, although evidence is limited. Considering the generalizability of the habituation process, we tested the degree to which organism-level behavioral and single cell manifestations were similar. Human embryonic kidney (HEK) cells that overexpressed an optogenetic actuator were photostimulated to test the effect of different stimulation protocols on cell responses. Depolarization induced by the photocurrent decreased successively over the stimulation protocol and the effect was reversible upon withdrawal of the stimulus. In addition to frequency- and intensity-dependent effects, the history of stimulations on the cells impacted subsequent depolarization in response to further stimulation. We identified tetraethylammonium (TEA)-sensitive native K⁺ channels as one of the mediators of this habituation phenotype. Finally, we used a theoretical model of habituation to elucidate some mechanistic aspects of the habituation response. In conclusion, we affirm that habituation is a time- and state-dependent biological strategy that can be adopted also by individual non-neuronal cells in response to repetitive stimuli.
Living systems are more robust, diverse, complex, and supportive of human life than any technology yet created. However, our ability to create novel lifeforms is currently limited to varying existing organisms or bioengineering organoids in vitro. Here we show a scalable pipeline for creating functional novel lifeforms: AI methods automatically design diverse candidate lifeforms in silico to perform some desired function, and transferable designs are then created using a cell-based construction toolkit to realize living systems with the predicted behaviors. Although some steps in this pipeline still require manual intervention, complete automation in future would pave the way to designing and deploying unique, bespoke living systems for a wide range of functions.
The generation of genomically stable and functional oocytes has great potential for preserving fertility and restoring ovarian function. It remains elusive whether functional oocytes can be generated from adult female somatic cells through reprogramming to germline-competent pluripotent stem cells (gPSCs) by chemical treatment alone. Here, we show that somatic granulosa cells isolated from adult mouse ovaries can be robustly induced to generate gPSCs by a purely chemical approach, with additional Rock inhibition and critical reprogramming facilitated by crotonic sodium or acid. These gPSCs acquired high germline competency and could consistently be directed to differentiate into primordial-germ-cell-like cells and form functional oocytes that produce fertile mice. Moreover, gPSCs promoted by crotonylation and the derived germ cells exhibited longer telomeres and high genomic stability like PGCs in vivo, providing additional evidence supporting the safety and effectiveness of chemical induction, which is particularly important for germ cells in genetic inheritance.
All epistemic agents physically consist of parts that must somehow comprise an integrated cognitive self. Biological individuals consist of subunits (organs, cells, and molecular networks) that are themselves complex and competent in their own native contexts. How do coherent biological Individuals result from the activity of smaller sub-agents? To understand the evolution and function of metazoan creatures’ bodies and minds, it is essential to conceptually explore the origin of multicellularity and the scaling of the basal cognition of individual cells into a coherent larger organism. In this article, I synthesize ideas in cognitive science, evolutionary biology, and developmental physiology toward a hypothesis about the origin of Individuality: “Scale-Free Cognition.” I propose a fundamental definition of an Individual based on the ability to pursue goals at an appropriate level of scale and organization and suggest a formalism for defining and comparing the cognitive capacities of highly diverse types of agents. Any Self is demarcated by a computational surface – the spatio-temporal boundary of events that it can measure, model, and try to affect. This surface sets a functional boundary - a cognitive “light cone” which defines the scale and limits of its cognition. I hypothesize that higher level goal-directed activity and agency, resulting in larger cognitive boundaries, evolve from the primal homeostatic drive of living things to reduce stress – the difference between current conditions and life-optimal conditions. The mechanisms of developmental bioelectricity - the ability of all cells to form electrical networks that process information - suggest a plausible set of gradual evolutionary steps that naturally lead from physiological homeostasis in single cells to memory, prediction, and ultimately complex cognitive agents, via scale-up of the basic drive of infotaxis. Recent data on the molecular mechanisms of pre-neural bioelectricity suggest a model of how increasingly sophisticated cognitive functions emerge smoothly from cell-cell communication used to guide embryogenesis and regeneration. This set of hypotheses provides a novel perspective on numerous phenomena, such as cancer, and makes several unique, testable predictions for interdisciplinary research that have implications not only for evolutionary developmental biology but also for biomedicine and perhaps artificial intelligence and exobiology.
The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in non-neural biological systems. Embryogenesis and regeneration require plasticity in many tissue types to achieve structural and functional goals in diverse circumstances. Thus, advances in both evolutionary cell biology and regenerative medicine require an understanding of how non-neural tissues could process information. Neurons evolved from ancient cell types that used bioelectric signaling to perform computation. However, it has not been shown whether or how non-neural bioelectric cell networks can support computation. We generalize connectionist methods to non-neural tissue architectures, showing that a minimal non-neural Bio-Electric Network (BEN) model that utilizes the general principles of bioelectricity (electrodiffusion and gating) can compute. We characterize BEN behaviors ranging from elementary logic gates to pattern detectors, using both fixed and transient inputs to recapitulate various biological scenarios. We characterize the mechanisms of such networks using dynamical-systems and information-theory tools, demonstrating that logic can manifest in bidirectional, continuous, and relatively slow bioelectrical systems, complementing conventional neural-centric architectures. Our results reveal a variety of non-neural decision-making processes as manifestations of general cellular biophysical mechanisms and suggest novel bioengineering approaches to construct functional tissues for regenerative medicine and synthetic biology as well as new machine learning architectures.
Anatomical homeostasis results from dynamic interactions between gene expression, physiology, and the external environment. Owing to its complexity, this cellular and organism-level phenotypic plasticity is still poorly understood. We establish planarian regeneration as a model for acquired tolerance to environments that alter endogenous physiology. Exposure to barium chloride (BaCl2) results in a rapid degeneration of anterior tissue in Dugesia japonica. Remarkably, continued exposure to fresh solution of BaCl2 results in regeneration of heads that are insensitive to BaCl2. RNA-seq revealed transcriptional changes in BaCl2-adapted heads that suggests a model of adaptation to excitotoxicity. Loss-of-function experiments confirmed several predictions: blockage of chloride and calcium channels allowed heads to survive initial BaCl2 exposure, inducing adaptation without prior exposure, whereas blockade of TRPM channels reversed adaptation. Such highly adaptive plasticity may represent an attractive target for biomedical strategies in a wide range of applications beyond its immediate relevance to excitotoxicity preconditioning.
Most, but not all cnidarian species in the class Hydrozoa have a life cycle in which a colonial, asexually reproducing hydroid phase alternates with a free-swimming, sexually reproducing medusa phase. They are not well known, in part because many of them are microscopic, at least in the medusa phase. Matching the two phases has previously required rearing of the organism from one phase to another, which has not often been possible. Here we show that DNA barcoding makes it possible to easily link life-cycle phases without the need for laboratory rearing. Hydrozoan medusae were collected by zooplankton tows in Newport Bay and the Pacific Ocean near Newport Beach, California, and hydroid colonies were collected from solid substrates in the same areas. Specimens were documented by videomicroscopy, preserved in ethanol, and sent to the Canadian Centre for DNA Barcoding at the University of Guelph, Ontario, Canada for sequencing of the COI DNA barcode. In the order Anthoathecata (athecate hydroids), DNA barcoding allowed for the discrimination between the medusae of eight putative species of Bougainvillia, and the hydroid stages were documented for two of these. The medusae of three putative species of Amphinema were identified, and the hydroid stages were identified for two of them. DNA barcodes were obtained from medusae of one species of Cladonema, one adult of the by-the wind Sailor, Velella velella, five putative species of Corymorpha with the matching hydroid phase for one; and Coryne eximia, Turritopsis dohrnii and Turritopsis nutricula with the corresponding hydroid phases. The actinula larvae and hydroid for the pink-hearted hydroid Ectopleura crocea were identified and linked by DNA barcoding. In the order Leptothecata (thecate hydroids) medusae were identified for Clytia elsaeoswaldae, Clytia gracilis and Clytia sp. 701 AC and matched with the hydroid phases for the latter two species. Medusae were matched with the hydroid phases for two species of Obelia (including O. dichotoma) and Eucheilota bakeri. Obelia geniculata was collected as a single hydroid. DNA barcodes were obtained for hydroids of Orthopyxis everta and three other species of Orthopyxis. One member of the family Solmarisidae, representing the order Narcomedusae, and one member (Liriope tetraphylla) of the order Trachymedusae were recognized as medusae. The results show the utility of DNA barcoding for matching life-cycle stages as well as for documenting the diversity of this class of organisms.
Medusae of Turritopsis dohrnii undergo reverse development in response to physical damage, adverse environmental conditions, or aging. Senescent, weakened or damaged medusae transform into a cluster of poorly differentiated cells (known as the cyst stage), which metamorphose back into a preceding life cycle stage, the polyp. During the metamorphosis, cell transdifferentiation occurs. The cyst represents the intermediate stage between a reverting medusa and a healthy polyp, during which cell transdifferentiation and tissue reorganization take place. Here we characterize and compare the transcriptomes of the typical polyp and newborn medusa stages of T. dohrnii with that of the cyst, to identify biological networks potentially involved in the reverse development and transdifferentiation processes. The polyp, medusa and cyst of T. dohrnii were sequenced through Illumina RNA-sequencing and assembled using a de novo approach, resulting in 92,569, 74,639 and 86,373 contigs, respectively. The transcriptomes were annotated and comparative analyses among the stages identified biological networks that were significantly over-and under-expressed in the cyst as compared to the polyp and medusa stages. Biological processes that occur at the cyst stage such as telomerase activity, regulation of transposable elements and DNA repair systems, and suppression of cell signaling pathways, mitotic cell division and cellular differentiation and development may be involved in T. dohrnii's reverse development and transdifferentiation. Our results are the first attempt to understand T. dohrnii's life-cycle reversal at the genetic level, and indicate possible avenues of future research on developmental strategies, cell transdifferentiation, and aging using T. dohrnii as a non-traditional in vivo system.
Patient-derived tumour xenografts and tumour organoids have become important preclinical model systems for cancer research. Both models maintain key features from their parental tumours, such as genetic and phenotypic heterogeneity, which allows them to be used for a wide spectrum of applications. In contrast to patient-derived xenografts, organoids can be established and expanded with high efficiency from primary patient material. On the other hand, xenografts retain tumour-stroma interactions, which are known to contribute to tumorigenesis. In this review, we discuss recent advances in patient-derived tumour xenograft and tumour organoid model systems and compare their promises and challenges as preclinical models in cancer research.
It has been 70 years since Donald Hebb published his formalized theory of synaptic adaptation during learning. Hebb’s seminal work foreshadowed some of the great neuroscientific discoveries of the following decades, including the discovery of long-term potentiation and other lasting forms of synaptic plasticity, and more recently the residence of memories in synaptically connected neuronal assemblies. Our understanding of the processes underlying learning and memory has been dominated by the view that synapses are the principal site of information storage in the brain. This view has received substantial support from research in several model systems, with the vast majority of studies on the topic corroborating a role for synapses in memory storage. Yet, despite the neuroscience community’s best efforts, we are still without conclusive proof that memories reside at synapses. Furthermore, an increasing number of non-synaptic mechanisms have emerged that are also capable of acting as memory substrates. In this review, we address the key findings from the synaptic plasticity literature that make these phenomena such attractive memory mechanisms. We then turn our attention to evidence that questions the reliance of memory exclusively on changes at the synapse and attempt to integrate these opposing views.
Habituation, a form of non‐associative learning, isno longer studied exclusively within the fields of psychology and neuroscience. Indeed, the same stimulus–response pattern is observed at the molecular, cellular, and organismal scales and is not dependent upon the presence of neurons. Hence, a more inclusive theory is required to accommodate aneural forms of habituation. Here an abstraction of the habituation process that does not rely upon particular biological pathways or substrates is presented. Instead, five generalizable elements that define the habituation process are operationalized. The formulation can be applied to interrogate systems as they respond to several stimulation paradigms, providing new insights and supporting existing behavioral data. The model can be used to deduce the relative contribution of elements that contribute to the measurable output of the system. The results suggest that habituation serves as a general biological strategy that any system can implement to adaptively respond to harmless, repetitive stimuli. Habituation can emerge from a rich repertoire of stimulations and biological systems; in the model, habituation is proposed as a convergent function to universally respond to repetitive stimuli. A minimal set of modular systems leading to habituation is described, thus providing a guide to recognize the process in a universal approach with any system.
Brains exhibit plasticity, multi-scale integration of information, computation and memory, having evolved by specialization of non-neural cells that already possessed many of the same molecular components and functions. The emerging field of basal cognition provides many examples of decision-making throughout a wide range of non-neural systems. How can biological information processing across scales of size and complexity be quantitatively characterized and exploited in biomedical settings? We use pattern regulation as a context in which to introduce the Cognitive Lens—a strategy using well-established concepts from cognitive and computer science to complement mechanistic investigation in biology. To facilitate the assimilation and application of these approaches across biology, we review tools from various quantitative disciplines, including dynamical systems, information theory and least-action principles. We propose that these tools can be extended beyond neural settings to predict and control systems-level outcomes, and to understand biological patterning as a form of primitive cognition. We hypothesize that a cognitive-level information-processing view of the functions of living systems can complement reductive perspectives, improving efficient top-down control of organism-level outcomes. Exploration of the deep parallels across diverse quantitative paradigms will drive integrative advances in evolutionary biology, regenerative medicine, synthetic bioengineering, cognitive neuroscience and artificial intelligence.
This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.
Learning and memory are indisputably key features of animal success. Using information about past experiences is critical for optimal decision-making in a fluctuating environment. Those abilities are usually believed to be limited to organisms with a nervous system, precluding their existence in non-neural organisms. However, recent studies showed that the slime mould Physarum polycephalum , despite being unicellular, displays habituation, a simple form of learning. In this paper, we studied the possible substrate of both short- and long-term habituation in slime moulds. We habituated slime moulds to sodium, a known repellent, using a 6 day training and turned them into a dormant state named sclerotia. Those slime moulds were then revived and tested for habituation. We showed that information acquired during the training was preserved through the dormant stage as slime moulds still showed habituation after a one-month dormancy period. Chemical analyses indicated a continuous uptake of sodium during the process of habituation and showed that sodium was retained throughout the dormant stage. Lastly, we showed that memory inception via constrained absorption of sodium for 2 h elicited habituation. Our results suggest that slime moulds absorbed the repellent and used it as a ‘circulating memory’.
This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.
Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. Here we argue that this necessitates a broad scientific research agenda to study machine behaviour that incorporates and expands upon the discipline of computer science and includes insights from across the sciences. We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour. Understanding the behaviour of the machines powered by artificial intelligence that increasingly mediate our social, cultural, economic and political interactions is essential to our ability to control the actions of these intelligent machines, reap their benefits and minimize their harms.
Electrical signaling in biology is typically associated with action potentials, transient spikes in membrane voltage that return to baseline. Here we show theoretically and experimentally that homogeneous or nearly homogeneous tissues can undergo spontaneous symmetry breaking into domains with different resting potentials, separated by stable bioelectrical domain walls. Transitions from one resting potential to another can occur through long-range migration of these domain walls. We map bioelectrical domain wall motion using all-optical electrophysiology in an engineered stable cell line and in human iPSC-derived myoblasts. Bioelectrical domain wall migration may occur during embryonic development and during physiological signaling processes in polarized tissues. These results demonstrate a novel form of bioelectrical pattern formation and long-range signaling.
Recent technological breakthroughs in our ability to derive and differentiate induced pluripotent stem cells, organoid biology, organ-on-chip assays, and 3-D bioprinting have all contributed to a heightened interest in the design, assembly, and manufacture of living systems with a broad range of potential uses. This white paper summarizes the state of the emerging field of “multi-cellular engineered living systems,” which are composed of interacting cell populations. Recent accomplishments are described, focusing on current and potential applications, as well as barriers to future advances, and the outlook for longer term benefits and potential ethical issues that need to be considered.
Studies suggest that the protozoan Toxoplasma gondii can disturb human behavior. This study aimed to systematically review the scientific literature on the possible associations between Toxoplasma gondii infection and neurobehavioral abnormalities in humans. We reviewed and summarized the studies published since 1990. The descriptors used were related to T. gondii infection and behavioral outcomes in humans; the main databases of the medical literature were accessed. The results of eight original articles published between 1994 and 2016 were evaluated and described. The most common serological method was the enzyme immunoassay. Most of the researchers used validated instruments for behavioral evaluation. Seven studies reported some association between the prevalence of anti-T. gondii antibodies and some altered behavioral aspects in adult humans; these studies focused on adult population in Europe and the USA. The most reported behavioral deviations are related to greater impulsivity and aggressiveness. There are very few studies on this subject, which present some limitations for inference and conclusions: most were cross-sectional studies, with a small sample size and in similar populations. Investigations with a larger sample size of different population groups should be performed to evaluate multiple factors.
Research into proximate and ultimate mechanisms of individual cognitive variation in animal populations is a rapidly growing field that incorporates physiological, behavioural and evolutionary investigations. Recent studies in humans and laboratory animals have shown that the enteric microbial community plays a central role in brain function and development. The ‘gut–brain axis’ represents a multi-directional signalling system that encompasses neurological, immunological and hormonal pathways. In particular it is tightly linked with the hypothalamic–pituitary–adrenal axis (HPA), a system that regulates stress hormone release and influences brain development and function. Experimental examination of the microbiome through manipulation of diet, infection, stress and exercise, suggests direct effects on cognition, including learning and memory. However, our understanding of these processes in natural populations is extremely limited. Here, we outline how recent advances in predominantly laboratory-based microbiome research can be applied to understanding individual differences in cognition. Experimental manipulation of the microbiome across natal and adult environments will help to unravel the interplay between cognitive variation and the gut microbial community. Focus on individual variation in the gut microbiome and cognition in natural populations will reveal new insight into the environmental and evolutionary constraints that drive individual cognitive variation.
This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive abilities’.
The precise nature of the engram, the physical substrate of memory, remains uncertain. Here, it is reported that RNA extracted from the central nervous system of Aplysia given long-term sensitization (LTS) training induced sensitization when injected into untrained animals; furthermore, the RNA-induced sensitization, like training-induced sensitization, required DNA methylation. In cellular experiments, treatment with RNA extracted from trained animals was found to increase excitability in sensory neurons, but not in motor neurons, dissociated from naïve animals. Thus, the behavioral, and a subset of the cellular, modifications characteristic of a form of nonassociative long-term memory (LTM) in Aplysia can be transferred by RNA. These results indicate that RNA is sufficient to generate an engram for LTS in Aplysia and are consistent with the hypothesis that RNA-induced epigenetic changes underlie memory storage in Aplysia.
Language users ordinarily suppose that they know what thoughts their own utterances express. We can call this supposed knowledge minimal self-knowledge. But what does it come to? And do we actually have it? Anti-individualism implies that the thoughts which a person's utterances express are partly determined by facts about their social and physical environments. If anti-individualism is true, then there are some apparently coherent sceptical hypotheses that conflict with our supposition that we have minimal self-knowledge. In this book, Anthony Brueckner and Gary Ebbs debate how to characterize this problem and develop opposing views of what it shows. Their discussion is the only sustained, in-depth debate about anti-individualism, scepticism and knowledge of one's own thoughts, and will interest both scholars and graduate students in philosophy of language, philosophy of mind and epistemology.
Large‐scale patterns of correlated growth in development are partially driven by competition for metabolic and informational resources. It is argued that competition between organs for limited resources is an important mesoscale morphogenetic mechanism that produces fitness‐enhancing correlated growth. At the genetic level, the growth of individual characters appears independent, or “modular,” because patterns of expression and transcription are often highly localized, mutations have trait‐specific effects, and gene complexes can be co‐opted as a unit to produce novel traits. However, body parts are known to interact over the course of ontogeny, and these reciprocal exchanges can be an important determinant of developmental outcomes. Genetic mechanisms underlie cell and tissue behaviors that allow organs to communicate with one another, but they also create evolutionarily adaptive competitive dynamics that are driven by physiological and biophysical processes. Advances in the understanding of competitive and closely related coordinative interactions across scales will complement existing research programs that emphasize the role of cellular mechanisms in morphogenesis. Study of the large‐scale order produced by competitive dynamics promises to facilitate advances in basic evolutionary and developmental biology, as well as applied research in fields such as bioengineering and regenerative medicine that aim to regulate patterning outcomes. Large‐scale patterns of correlated growth are partially driven by adaptive competition for metabolic and informational molecules. Evolution exploits the use of limiting resources by organs as a kind of stigmergy, enabling coordination without direct communication. Such competition is an important mesoscale morphogenetic mechanism that produces fitness‐enhancing coordinated morphogenesis.
Some animals, such as planaria, can regenerate complex anatomical structures, in a process regulated by genetic and biophysical factors, but additional external inputs into regeneration remain to be uncovered. Microbial communities inhabiting metazoan organisms are important for metabolic, immune, and disease processes, but their instructive influence over host structures remains largely unexplored. Here, we show that Aquitalea sp. FJL05, an endogenous commensal bacterium of Dugesia japonica planarians, and one of the small molecules it produces, indole, can influence axial and head patterning during regeneration, leading to regeneration of permanently two-headed animals. Testing the impact of indole on planaria tissues via RNA sequencing, we find that indole alters the regenerative outcomes in planarians through changes in expression to patterning genes, including a downregulation of Wnt pathway genes. These data provide a unique example of the product of a commensal bacterium modulating transcription of patterning genes to affect the host's anatomical structure during regeneration.
In February 2020, the European Molecular Biology Laboratory (EMBL) and the Institute for Bioengineering of Catalonia (IBEC) joined forces to unite researchers from all over the globe to discuss emerging topics in 'Engineering Multicellular Systems'. As we review here, key themes that arose throughout the meeting included the ethics of organoids in developmental biology, bottom-up versus top-down models, tissue organizing principles, and the future of improving these systems to better mimic the natural world.
Cellular membrane potential plays a key role in the formation and retrieval of memories in the metazoan brain, but it remains unclear whether such memory can also be encoded in simpler organisms like bacteria. Here, we show that single-cell-level memory patterns can be imprinted in bacterial biofilms by light-induced changes in the membrane potential. We demonstrate that transient optical perturbations generate a persistent and robust potassium-channel-mediated change in the membrane potential of bacteria within the biofilm. The light-exposed cells respond in an anti-phase manner, relative to unexposed cells, to both natural and induced oscillations in extracellular ion concentrations. This anti-phase response, which persists for hours following the transient optical stimulus, enables a direct single-cell resolution visualization of spatial memory patterns within the biofilm. The ability to encode robust and persistent membrane-potential-based memory patterns could enable computations within prokaryotic communities and suggests a parallel between neurons and bacteria.
Polyploid giant cancer cells, either multinucleated or mononucleated, in high grade serous carcinoma of the ovary have been previously recognized. Different theories including degenerative changes or an important step in the development of high grade serous carcinoma have been proposed. Here we investigate possible explanations for the presence of multinucleated and giant cells in high grade serous carcinoma. We reviewed 33 cases of ovarian high grade serous carcinoma (12 stage I, 7 stage II, and 14 stage III). We counted the number of polyploid giant cancer cells in 20 consecutive 10× fields. In 11 cases where polyploid giant cancer cells were easily found, immunohistochemistry for Ki67, p53, and OCT 3/4 was performed. Patients with polyploid giant cancer cells were older than those without. Polyploid giant cancer cells were more frequent in stage I lesions (75%) than in stages II or III (57% in both) and less frequent in metastases compared with primary ovarian tumors. Mitotic figures were present in regular sized cells but were absent in polyploid giant cancer cells. OCT3/4 was negative in all cases assessed. In 8 cases, more than 70% of the mononuclear cells were positive for Ki-67, similar to the percentage of Ki67 positive cells in multinucleated giant cells. p53 had a perfect correlation in regular sized mononuclear cancer cells and in polyploid giant cancer cells.
Polyploid giant cancer cells are neither degenerative cells nor traditional cancer stem cells but most probably represent an intermediate step between stem cells and mature tumor cells formed by endoreplication.
Nervous systems are traditionally thought of as providing sensing and behavioral coordination functions at the level of the whole organism. What is the evolutionary origin of the mechanisms enabling the nervous systems' information processing ability? Here, we review evidence from evolutionary, developmental, and regenerative biology suggesting a deeper, ancestral function of both pre-neural and neural cell-cell communication systems: the long-distance coordination of cell division and differentiation required to create and maintain body-axis symmetries. This conceptualization of the function of nervous system activity sheds new light on the evolutionary transition from the morphologically rudimentary, non-neural Porifera and Placazoa to the complex morphologies of Ctenophores, Cnidarians, and Bilaterians. It further allows a sharp formulation of the distinction between long-distance axis-symmetry coordination based on external coordinates, e.g., by whole-organism scale trophisms as employed by plants and sessile animals, and coordination based on body-centered coordinates as employed by motile animals. Thus we suggest that the systems that control animal behavior evolved from ancient mechanisms adapting preexisting ionic and neurotransmitter mechanisms to regulate individual cell behaviors during morphogenesis. An appreciation of the ancient, non-neural origins of bioelectrically mediated computation suggests new approaches to the study of embryological development, including embryological dysregulation, cancer, regenerative medicine, and synthetic bioengineering.
Complex behavior is associated with animals with nervous systems, but decision-making and learning also occur in non-neural organisms [1], including singly nucleated cells [2-5] and multi-nucleate synctia [6-8]. Ciliates are single-cell eukaryotes, widely dispersed in aquatic habitats [9], with an extensive behavioral repertoire [10-13]. In 1906, Herbert Spencer Jennings [14, 15] described in the sessile ciliate Stentor roeseli a hierarchy of responses to repeated stimulation, which are among the most complex behaviors reported for a singly nucleated cell [16, 17]. These results attracted widespread interest [18, 19] and exert continuing fascination [7, 20-22] but were discredited during the behaviorist orthodoxy by claims of non-reproducibility [23]. These claims were based on experiments with the motile ciliate Stentor coeruleus. We acquired and maintained the correct organism in laboratory culture and used micromanipulation and video microscopy to confirm Jennings' observations. Despite significant individual variation, not addressed by Jennings, S. roeseli exhibits avoidance behaviors in a characteristic hierarchy of bending, ciliary alteration, contractions, and detachment, which is distinct from habituation or conditioning. Remarkably, the choice of contraction versus detachment is consistent with a fair coin toss. Such behavioral complexity may have had an evolutionary advantage in protist ecosystems, and the ciliate cortex may have provided mechanisms for implementing such behavior prior to the emergence of multicellularity. Our work resurrects Jennings' pioneering insights and adds to the list of exceptional features, including regeneration [24], genome rearrangement [25], codon reassignment [26], and cortical inheritance [27], for which the ciliate clade is renowned.
Some parasites can manipulate the behavior of their animal hosts to increase transmission. An interesting area of research is understanding how host neurobiology is manipulated by microbes to the point of displaying such aberrant behaviors. Here, we characterize the metabolic profile of the brain of an insect at the moment of the behavioral manipulation by a parasitic microbe. Our model system are ants infected with the parasitic fungus Ophiocordyceps kimflemingiae (=unilateralis), which manipulates ants to climb and bite into plant substrates, before killing the host (i.e. zombie ants). At the moment of the behavioral manipulation by the fungus, the host's brain is not invaded by the fungus which is known to extensively invade muscle tissue. We found that, despite not being invaded by the parasite, the brains of manipulated ants are notably different, showing alterations in neuromodulatory substances, signs of neurodegeneration, changes in energy use, and antioxidant compound that signal stress reactions by the host. Ergothionine, a fungal derived compound with known neuronal cytoprotection functions was found to be highly elevated in zombie ant brains suggesting the fungus, which does not invade the central nervous system, is preserving the brain.
The EMBO-EMBL Symposium 'Synthetic Morphogenesis: From Gene Circuits to Tissue Architecture' was held in Heidelberg, Germany, in March 2019, with 150 participants seeking to reverse-engineer embryogenesis, emphasizing quantitative simulation and the use of synthetic systems to test models. This highly dynamic, interdisciplinary mix of quantitative developmental genetics, bioengineering, synthetic biology and artificial life aimed to reveal how evolution exploits physical forces and genetics to implement the cell- and tissue-level decision-making required for complex morphogenesis.
There are numerous examples of parasites that manipulate the behavior of the hosts that they infect. One such host-pathogen relationship occurs between the 'zombie-ant fungus' Ophiocordyceps unilateralis sensu lato and its carpenter ant host. Infected ants climb to elevated locations and bite onto vegetation where they remain permanently affixed well after death. The mandibular muscles, but not the brain, of infected ants are extensively colonized by the fungus. We sought to investigate the mechanisms by which O. unilateralis s.l. may be able to influence mandibular muscle contraction despite widespread muscle damage. We found that infected muscles show evidence of hypercontraction. Despite the extensive colonization, both motor neurons and neuromuscular junctions appear to be maintained. Infection results in sarcolemmal damage, but this is not specific to the death grip. We found evidence of precise penetration of muscles by fungal structures and the presence of extracellular vesicle-like particles, both of which may contribute to mandibular hypercontraction.
Although it is well established that some organisms can regenerate lost structures, the ability to remodel existing malformed structures has been less well studied. Therefore, in this study we examined the ability of pre-metamorphic Xenopus laevis tadpoles to self-correct malformed craniofacial tissues. We found that tadpoles can adaptively improve and normalize abnormal craniofacial morphology caused by numerous developmental perturbations. We then investigated the tissue-level and molecular mechanisms that mediate the self-correction of craniofacial defects in pre-metamorphic X. laevis tadpoles. Our studies revealed that this adaptive response involves morphological changes and the remodeling of cartilage tissue, prior to metamorphosis. RT-qPCR and RNA-seq analysis of gene expression suggests a thyroid hormone-independent endocrine signaling pathway as the potential mechanism responsible for triggering the adaptive and corrective remodeling response in these larvae that involves mmp1 and mmp13 upregulation. Thus, investigating how malformed craniofacial tissues are naturally corrected in X. laevis tadpoles has provided valuable insights into the maintenance and manipulation of craniofacial morphology in a vertebrate system. These insights may help in the development of novel therapies for developmental craniofacial anomalies in humans.
Recent advances in molecular biology such as gene editing [1], bioelectric recording and manipulation [2] and live cell microscopy using fluorescent reporters [3], [4] – especially with the advent of light-controlled protein activation through optogenetics [5] – have provided the tools to measure and manipulate molecular signaling pathways with unprecedented spatiotemporal precision. This has produced ever increasing detail about the molecular mechanisms underlying development and regeneration in biological organisms. However, an overarching concept – that can predict the emergence of form and the robust maintenance of complex anatomy – is largely missing in the field. Classic (i.e., dynamic systems and analytical mechanics) approaches such as least action principles are difficult to use when characterizing open, far-from equilibrium systems that predominate in Biology. Similar issues arise in neuroscience when trying to understand neuronal dynamics from first principles. In this (neurobiology) setting, a variational free energy principle has emerged based upon a formulation of self-organization in terms of (active) Bayesian inference. The free energy principle has recently been applied to biological self-organization beyond the neurosciences [6], [7]. For biological processes that underwrite development or regeneration, the Bayesian inference framework treats cells as information processing agents, where the driving force behind morphogenesis is the maximization of a cell's model evidence. This is realized by the appropriate expression of receptors and other signals that correspond to the cell's internal (i.e., generative) model of what type of receptors and other signals it should express. The emerging field of the free energy principle in pattern formation provides an essential quantitative formalism for understanding cellular decision-making in the context of embryogenesis, regeneration, and cancer suppression. In this paper, we derive the mathematics behind Bayesian inference – as understood in this framework – and use simulations to show that the formalism can reproduce experimental, top-down manipulations of complex morphogenesis. First, we illustrate this ‘first principle’ approach to morphogenesis through simulated alterations of anterior-posterior axial polarity (i.e., the induction of two heads or two tails) as in planarian regeneration. Then, we consider aberrant signaling and functional behavior of a single cell within a cellular ensemble – as a first step in carcinogenesis as false ‘beliefs’ about what a cell should ‘sense’ and ‘do’. We further show that simple modifications of the inference process can cause – and rescue – mis-patterning of developmental and regenerative events without changing the implicit generative model of a cell as specified, for example, by its DNA. This formalism offers a new road map for understanding developmental change in evolution and for designing new interventions in regenerative medicine settings.
Tissue remodeling is broadly defined as the reorganization or restoration of existing tissues. Tissue remodeling processes are responsible for directing the development and maintenance of tissues, organs, and overall morphology of an organism. Therefore, studying the regulatory and mechanistic aspects of tissue remodeling allows one to decipher how tissue structure and function is manipulated in animals. As such, research focused on investigating natural tissue reorganization in animal model organisms has great potential for advancing medical therapies, in conjunction with tissue engineering and regenerative medicine. Here we discuss the molecular and cellular mechanisms responsible for tissue remodeling events that occur across several animal phyla. Notably, this review emphasizes the molecular and cellular mechanisms involved in embryonic and postnatal physiological tissue remodeling events, ranging from metamorphosis to bone remodeling during functional adaptation.
The question of how general anesthetics suppress consciousness has persisted since the mid-19th century, but it is only relatively recently that the field has turned its focus to a systematic understanding of emergence. Once assumed to be a purely passive process, spontaneously occurring as residual levels of anesthetics dwindle below a critical value, emergence from general anesthesia has been reconsidered as an active and controllable process. Emergence is driven by mechanisms that can be distinct from entry to the anesthetized state. In this narrative review, we focus on the burgeoning scientific understanding of anesthetic emergence, summarizing current knowledge of the neurotransmitter, neuromodulators, and neuronal groups that prime the brain as it prepares for its journey back from oblivion. We also review evidence for possible strategies that may actively bias the brain back toward the wakeful state.
Axial patterning during planarian regeneration relies on a transcriptional circuit that confers distinct positional information on the two ends of an amputated fragment. The earliest known elements of this system begin demarcating differences between anterior and posterior wounds by 6 h postamputation. However, it is still unknown what upstream events break the axial symmetry, allowing a mutual repressor system to establish invariant, distinct biochemical states at the anterior and posterior ends. Here, we show that bioelectric signaling at 3 h is crucial for the formation of proper anterior-posterior polarity in planaria. Briefly manipulating the endogenous bioelectric state by depolarizing the injured tissue during the first 3 h of regeneration alters gene expression by 6 h postamputation and leads to a double-headed phenotype upon regeneration despite confirmed washout of ionophores from tissue. These data reveal a primary functional role for resting membrane potential taking place within the first 3 h after injury and kick-starting the downstream pattern of events that elaborate anatomy over the following 10 days. We propose a simple model of molecular-genetic mechanisms to explain how physiological events taking place immediately after injury regulate the spatial distribution of downstream gene expression and anatomy of regenerating planaria.
Morphogenesis allows millions of cells to self-organize into intricate structures with a wide variety of functional shapes during embryonic development. This process emerges from local interactions of cells under the control of gene circuits that are identical in every cell, robust to intrinsic noise, and adaptable to changing environments. Constructing human technology with these properties presents an important opportunity in swarm robotic applications ranging from construction to exploration. Morphogenesis in nature may use two different approaches: hierarchical, top-down control or spontaneously self-organizing dynamics such as reaction-diffusion Turing patterns. Here, we provide a demonstration of purely self-organizing behaviors to create emergent morphologies in large swarms of real robots. The robots achieve this collective organization without any self-localization and instead rely entirely on local interactions with neighbors. Results show swarms of 300 robots that self-construct organic and adaptable shapes that are robust to damage. This is a step toward the emergence of functional shape formation in robot swarms following principles of self-organized morphogenetic engineering.
Little is known about how individual cells sense the macroscopic geometry of their tissue environment. Here, we explore whether long-range electrical signaling can convey information on tissue geometry to individual cells. First, we studied an engineered electrically excitable cell line. Cells grown in patterned islands of different shapes showed remarkably diverse firing patterns under otherwise identical conditions, including regular spiking, period-doubling alternans, and arrhythmic firing. A Hodgkin-Huxley numerical model quantitatively reproduced these effects, showing how the macroscopic geometry affected the single-cell electrophysiology via the influence of gap junction-mediated electrical coupling. Qualitatively similar geometry-dependent dynamics were observed in human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. The cardiac results urge caution in translating observations of arrhythmia in vitro to predictions in vivo, where the tissue geometry is very different. We study how to extrapolate electrophysiological measurements between tissues with different geometries and different gap junction couplings.
The molecular networks plant cells evolved to tune their development in response to the environment are becoming increasingly well understood. Much less is known about how these programs function in the multicellular context of organs and the impact this spatial embedding has on emergent decision-making. Here I address these questions and investigate whether the computational control principles identified in engineered information processing systems also apply to plant development. Examples of distributed computing underlying plant development are presented and support the presence of shared mechanisms of information processing across these domains. The coinvestigation of computation across plant biology and computer science can provide novel insight into the principles of plant development and suggest novel algorithms for use in distributed computing.
Signal transmission among cells enables long-range coordination in biological systems. However, the scarcity of quantitative measurements hinders the development of theories that relate signal propagation to cellular heterogeneity and spatial organization. We address this problem in a bacterial community that employs electrochemical cell-to-cell communication. We developed a model based on percolation theory, which describes how signals propagate through a heterogeneous medium. Our model predicts that signal transmission becomes possible when the community is organized near a critical phase transition between a disconnected and a fully connected conduit of signaling cells. By measuring population-level signal transmission with single-cell resolution in wild-type and genetically modified communities, we confirm that the spatial distribution of signaling cells is organized at the predicted phase transition. Our findings suggest that at this critical point, the population-level benefit of signal transmission outweighs the single-cell level cost. The bacterial community thus appears to be organized according to a theoretically predicted spatial heterogeneity that promotes efficient signal transmission.
By probing early embryogenesis and regeneration, interspecies chimeras provide a unique platform for discovery and clinical use. Although efficient generation of human:animal chimeric embryos remains elusive, recent advancements attempt to overcome incompatibilities in xenogeneic development and transplantation.
Planarian behavior, physiology, and pattern control offer profound lessons for regenerative medicine, evolutionary biology, morphogenetic engineering, robotics, and unconventional computation. Despite recent advances in the molecular genetics of stem cell differentiation, this model organism's remarkable anatomical homeostasis provokes us with truly fundamental puzzles about the origin of large-scale shape and its relationship to the genome. In this review article, we first highlight several deep mysteries about planarian regeneration in the context of the current paradigm in this field. We then review recent progress in understanding of the physiological control of an endogenous, bioelectric pattern memory that guides regeneration, and how modulating this memory can permanently alter the flatworm's target morphology. Finally, we focus on computational approaches that complement reductive pathway analysis with synthetic, systems-level understanding of morphological decision-making. We analyze existing models of planarian pattern control and highlighting recent successes and remaining knowledge gaps in this interdisciplinary frontier field.