Phenomenological architecture of a mind and operational architectonics of the brain: The unified metastable continuum
Abstract
In our contribution we will observe phenomenal architecture of a mind and operational architectonics of the brain and will show their intimate connectedness within a single integrated metastable continuum. The notion of operation of different complexity is the fundamental and central one in bridging the gap between brain and mind: it is precisely by means of this notion that it is possible to identify what at the same time belongs to the phenomenal conscious level and to the neurophysiological level of brain activity organization, and what mediates between them. Implications for linguistic semantics, self-organized distributed computing algorithms, artificial machine consciousness, and diagnosis of dynamic brain diseases will be discussed briefly.
... Note that here, the notion of "phenomena" here is different than that used by e.g.[105] where phenomenological architecture and properties are regarded as a representation of environment in the first-person mind[106], complementary to "physiological" architecture in the brain-as in[107]. ...
The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-twentieth century. Recent developments in artificial intelligence (AI) have accelerated this progress. This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
The problem of explaining the relationship between subjective experience and physical reality remains difficult and unresolved. In most explanations, consciousness is epiphenomenal, without causal power. The most notable exception is Integrated Information Theory (IIT), which provides a causal explanation for consciousness. However, IIT relies on an identity between subjectivity and a particular type of physical structure, namely with an information structure that has intrinsic causal power greater than the sum of its parts. Any theory that relies on a psycho-phyiscal identity must eventually appeal to panpsychism, which undermines that theory’s claim to be fundamental. IIT has recently pivoted towards a strong version of causal emergence, but macroscopic structures cannot be stronger causally than their microphysical parts without some new physical law or governing principle. The approach taken here is designed to uncover such a principle. The decisive argument is entirely deductive from initial premises that are phenomenologically certain. If correct, the arguments prove that conscious experience is sufficient to create additional degrees of causal freedom independently of the content of experience, and in a manner that is unpredictable and unobservable by any temporally sequential means. This provides a fundamental principle about consciousness, and a conceptual bridge between it and the physics describing what is experienced. The principle makes testable predictions about brain function, with notable differences from IIT, some of which are also empirically testable.
Unprecedented advancements in the diagnosis and treatment of patients with disorders of consciousness (DoC) have given rise to ethical questions about how to recognize and respect autonomy and a sense of agency of the personhood when those capacities are themselves disordered, as they typically are in patients with DoC. At the intersection of these questions rests the distinction between consciousness and unconsciousness. Indeed, evaluations of consciousness levels and capacity for recovery have a significant impact on decisions regarding whether to discontinue or prolong life-sustaining therapy for DoC patients. However, in the unconsciousness domain, there is the confusing array of terms that are regularly used interchangeably, making it quite challenging to comprehend what unconsciousness is and how it might be empirically grounded. In this opinion paper, we will provide a brief overview of the state of the field of unconsciousness and show how a rapidly evolving electroencephalogram (EEG) neuroimaging technique may offer empirical, theoretical, and practical tools to approach unconsciousness and to improve our ability to distinguish consciousness from unconsciousness and also nonconsciousness with greater precision, particularly in cases that are borderline (as is typical in patients with DoC). Furthermore, we will provide a clear description of three distant notions of (un)consciousness (unconsciousness, nonconsciousness, and subconsciousness) and discuss how they relate to the experiential selfhood which is essential for comprehending the moral significance of what makes life worth living.
Issues concerning the unity of minds, bodies and the world have often recurred in the history of philosophy and, more recently, in scientific models. Taking into account both the philosophical and scientific knowledge about consciousness, this book presents and discusses some theoretical guiding ideas for the science of consciousness. The authors argue that, within this interdisciplinary context, a consensus appears to be emerging assuming that the conscious mind and the functioning brain are two aspects of a complex system that interacts with the world. How can this concept of reality - one that includes the existence of consciousness - be approached both philosophically and scientifically? The Unity of Mind, Brain and World is the result of a three-year online discussion between the authors who present a diversity of perspectives, tending towards a theoretical synthesis, aimed to contribute to the insertion of this field of knowledge in the academic curriculum.
Issues concerning the unity of minds, bodies and the world have often recurred in the history of philosophy and, more recently, in scientific models. Taking into account both the philosophical and scientific knowledge about consciousness, this book presents and discusses some theoretical guiding ideas for the science of consciousness. The authors argue that, within this interdisciplinary context, a consensus appears to be emerging assuming that the conscious mind and the functioning brain are two aspects of a complex system that interacts with the world. How can this concept of reality - one that includes the existence of consciousness - be approached both philosophically and scientifically? The Unity of Mind, Brain and World is the result of a three-year online discussion between the authors who present a diversity of perspectives, tending towards a theoretical synthesis, aimed to contribute to the insertion of this field of knowledge in the academic curriculum.
Issues concerning the unity of minds, bodies and the world have often recurred in the history of philosophy and, more recently, in scientific models. Taking into account both the philosophical and scientific knowledge about consciousness, this book presents and discusses some theoretical guiding ideas for the science of consciousness. The authors argue that, within this interdisciplinary context, a consensus appears to be emerging assuming that the conscious mind and the functioning brain are two aspects of a complex system that interacts with the world. How can this concept of reality - one that includes the existence of consciousness - be approached both philosophically and scientifically? The Unity of Mind, Brain and World is the result of a three-year online discussion between the authors who present a diversity of perspectives, tending towards a theoretical synthesis, aimed to contribute to the insertion of this field of knowledge in the academic curriculum.
Many practicing clinicians are time-poor and are unaware of the accumulated neuroscience developments. Additionally, given the conservative nature of their field, key insights and findings trickle through into the mainstream clinical zeitgeist rather slowly. Over many decades, clinical, systemic, and cognitive neuroscience have produced a large and diverse body of evidence for the potential utility of brain activity (measured by electroencephalogram—EEG) for neurology and psychiatry. Unfortunately, these data are enormous and essential information often gets buried, leaving many researchers stuck with outdated paradigms. Additionally, the lack of a conceptual and unifying theoretical framework, which can bind diverse facts and relate them in a meaningful way, makes the whole situation even more complex. To contribute to the systematization of essential data (from the authors’ point of view), we present an overview of important findings in the fields of electrophysiology and clinical, systemic, and cognitive neuroscience and provide a general theoretical–conceptual framework that is important for any application of EEG signal analysis in neuropsychopathology. In this context, we intentionally omit detailed descriptions of EEG characteristics associated with neuropsychopathology as irrelevant to this theoretical–conceptual review.
This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
The brain’s ability to create a unified conscious representation of an object by integrating information from multiple perception pathways is called perceptual binding. Binding is crucial for normal cognitive function. Some perceptual binding errors and disorders have been linked to certain neurological conditions, brain lesions, and conditions that give rise to illusory conjunctions. However, the mechanism of perceptual binding remains elusive. Here, I present a computational model of binding using two sets of coupled oscillatory processes that are assumed to occur in response to two different percepts. I use the model to study the dynamic behavior of coupled processes to characterize how these processes can modulate each other and reach a temporal synchrony. I identify different oscillatory dynamic regimes that depend on coupling mechanisms and parameter values. The model can also discriminate different combinations of initial inputs that are set by initial states of coupled processes. Decoding brain signals that are formed through perceptual binding is a challenging task, but my modeling results demonstrate how crosstalk between two systems of processes can possibly modulate their outputs. Therefore, my mechanistic model can help one gain a better understanding of how crosstalk between perception pathways can affect the dynamic behavior of the systems that involve perceptual binding.
The goal of this work is to compile the basic components for the construction of an electromagnetic field theory of consciousness that meets the standards of a fundamental theory. An essential cornerstone of the conceptual framework is the vacuum state of quantum electrodynamics which, contrary to the classical notion of the vacuum, can be viewed as a vibrant ocean of energy, termed zero-point field (ZPF). Being the fundamental substrate mediating the electromagnetic force, the ubiquitous ZPF constitutes the ultimate bedrock of all electromagnetic phenomena. In particular, resonant interaction with the ZPF is critical for understanding rapidly forming, long-range coherent activity patterns that are characteristic of brain dynamics. Assuming that the entire phenomenal color palette is rooted in the vibrational spectrum of the ZPF and that each normal mode of the ZPF is associated with an elementary shade of consciousness, it stands to reason that conscious states are caused by the coupling of the brain to a particular set of normal modes selectively filtered from the full frequency spectrum of the ZPF. From this perspective, the brain is postulated to function as a resonant oscillator that couples to a specific range of ZPF modes, using these modes as a keyboard for the composition of an enormous variety of phenomenal states. Theoretical considerations suggest that the brain-ZPF interface is controlled by altering the concentrations of neurotransmitters, placing the detailed study of the neurotransmitter-ZPF interaction at the center of future research activities.
Metastability has been proposed as a new principle of behavioral and brain function and may point the way to a truly complementary neuroscience. From elementary coordination dynamics we show explicitly that metastability is a result of a symmetry breaking caused by the subtle interplay of two forces: the tendency of the components to couple together and the tendency of the components to express their intrinsic independent behavior. The metastable regime reconciles the well-known tendencies of specialized brain regions to express their autonomy (segregation) and the tendencies for those regions to work together as a synergy (integration). Integration ∼ segregation is just one of the complementary pairs (denoted by the tilde (∼) symbol) to emerge from the science of coordination dynamics. We discuss metastability in the brain by describing the favorable conditions existing for its emergence and by deriving some predictions for its empirical characterization in neurophysiological recordings.
New imaging techniques in cognitive neuroscience have produced a deluge of information correlating cognitive and neural phenomena. Yet our understanding of the inter-relationship between brain and mind remains hampered by the lack of a theoretical language for expressing cognitive functions in neural terms. We propose an approach to understanding operational laws in cognition based on principles of coordination dynamics that are derived from a simple and experimentally verified theoretical model. When applied to the dynamical properties of cortical areas and their coordination, these principles support a mechanism of adaptive inter-area pattern constraint that we postulate underlies cognitive operations generally.
Visual awareness is a favorable form of consciousness to study neurobiologically. We propose that it takes two forms: a very fast form, linked to iconic memory, that may be difficult to study; and a somewhat slower one involving visual attention and short-term memory. In the slower form an attentional
mechanism transiently binds together all those neurons whose activity relates to the relevant features of a single visual object. We suggest this is done by generating coherent semi-synchronous oscillations, probably in the 40-70 Hz range. These oscillations then activate a transient short-term
(working) memory. We outfit several lines of experimental work that might advance the understanding of the neural mechanisms involved. The neural basis of very short-term memory especially needs more experimental study.
This chapter is devoted to one of the most interesting applications of non-parametric statistical diagnosis, namely, to the analysis of the human brain’s electrical activity (the electroencephalogram, or EEG). The meaning and the features of the EEG, as well as the problems arising from the high non-stationarity of the EEG signal, are reviewed. We present experimental results demonstrating the application of the statistical diagnosis methods described in this book to the EEG, and discuss the prospects for further development of the change-point detection methodology with the emphasis on the estimation of coupling between different signal channels.
Haikonen envisions autonomous robots that perceive and understand the world directly, acting in it in a natural human-like way without the need of programs and numerical representation of information. By developing higher-level cognitive functions through the power of artificial associative neuron architectures, the author approaches the issues of machine consciousness. Robot Brains expertly outlines a complete system approach to cognitive machines, offering practical design guidelines for the creation of non-numeric autonomous creative machines. It details topics such as component parts and realization principles, so that different pieces may be implemented in hardware or software. Real-world examples for designers and researchers are provided, including circuit and systems examples that few books on this topic give. In novel technical and practical detail, this book also considers: the limitations and remedies of traditional neural associators in creating true machine cognition; basic circuit assemblies cognitive neural architectures; how motors can be interfaced with the associative neural system in order for fluent motion to be achieved without numeric computations; memorization, imagination, planning and reasoning in the machine; the concept of machine emotions for motivation and value systems; an approach towards the use and understanding of natural language in robots. The methods presented in this book have important implications for computer vision, signal processing, speech recognition and other information technology fields. Systematic and thoroughly logical, it will appeal to practising engineers involved in the development and design of robots and cognitive machines, also researchers in Artificial Intelligence. Postgraduate students in computational neuroscience and robotics, and neuromorphic engineers will find it an exciting source of information.
Theoretical, methodological and methodical aspects on nonstationary EEGs, that is, EEGs whose patterns undergo changes with time are reviewed. The piecewise description of the EEG is confirmed on the base of the own data and results of another investigators. It is analysed the basic approach to statistical evaluation of nonstationary EEGs in the terms of quasi-stationary segments. The special attention is devoted tho the data of segmental organization of the bioelectrical field of the cortex. Taking into consideration new experimental data concerning the time consistence of the segmental descriptions of regional EEGs it is suggested conception of "operational synchrony" as a form of discretial cooperation of cortex process. The theoretical explanations of the phenomenology for pies-wise functioning of neuron nets are discussed.
The concept of neurobehavioral dynamical system (NBDS) is introduced as a unifying explanation of the following facts of neural and behavioral patterns generation, namely: 1) that numerous physical mechanisms are capable of realizing the same neural and behavioral patterns; 2) that the same network can produce multiple patterns, a feature known as multifunctionality; and, 3) that networks can switch flexibly and spontaneously from one configuration to another under certain influences. Synergetic phase transitions provide the methodological strategy through which to discover laws of neural and behavioral pattern generation. At transitions, patterns arise in a self-organized fashion, as collective states produced by coupled nonlinear dynamics. Identified laws: 1) possess so-called ‘universal’ properties, governing dynamical behavior on several scales of observation (e.g. individual neurons, neural networks, kinematics...) and in different systems (thereby accounting for fact #1 above); 2) exhibit multistability and bifurcation depending on parameter values (fact #2 above); and 3) are stochastic, fluctuations playing a key role in probing the stability of the pattern dynamics and promoting labile change (fact #3). In a NBDS, it is not necessary to posit a separate pattern generator for each observed behavior. Rather, where the system “lives” in the parameter space of the law, determines whether ordered or irregular patterns are observed. Linkage among different levels of description is by virtue of shared dynamical laws, which incorporate both chance and choice.
In the present review, an integrated approach to craving and addiction is discussed, which is based on recent insights from psychology and neuropsychopharmacology. An integrated model explains craving and relapse in humans by the psychological mechanism of "attentional bias" and provides neuropsychopharmacological mechanisms for this bias. According to this model, cognitive processes mediate between drug stimulus and the subject's response to this stimulus and subsequent behavioral response (e.g., drug use, relapse). According to the model, a conditioned drug stimulus produces an increase in dopamine levels in the corticostriatal circuit, in particular the anterior cingulate gyrus, amygdala, and nucleus accumbens, which in turn serves to draw the subject's attention towards a perceived drug stimulus. This process results in motor preparation and a hyperattentive state towards drug-related stimuli that, ultimately, promotes further craving and relapse. Evidence for this attentional bias hypothesis is reviewed from both the psychopharmacological and the neuroanatomical viewpoints. The attentional bias hypothesis raises several suggestions for clinical approaches and further research.