ArticleLiterature Review

Seven challenges for neuroscience

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Abstract

Although twenty-first century neuroscience is a major scientific enterprise, advances in basic research have not yet translated into benefits for society. In this paper, I outline seven fundamental challenges that need to be overcome. First, neuroscience has to become "big science" - we need big teams with the resources and competences to tackle the big problems. Second, we need to create interlinked sets of data providing a complete picture of single areas of the brain at their different levels of organization with "rungs" linking the descriptions for humans and other species. Such "data ladders" will help us to meet the third challenge - the development of efficient predictive tools, enabling us to drastically increase the information we can extract from expensive experiments. The fourth challenge goes one step further: we have to develop novel hardware and software sufficiently powerful to simulate the brain. In the future, supercomputer-based brain simulation will enable us to make in silico manipulations and recordings, which are currently completely impossible in the lab. The fifth and sixth challenges are translational. On the one hand we need to develop new ways of classifying and simulating brain disease, leading to better diagnosis and more effective drug discovery. On the other, we have to exploit our knowledge to build new brain-inspired technologies, with potentially huge benefits for industry and for society. This leads to the seventh challenge. Neuroscience can indeed deliver huge benefits but we have to be aware of widespread social concern about our work. We need to recognize the fears that exist, lay them to rest, and actively build public support for neuroscience research. We have to set goals for ourselves that the public can recognize and share. And then we have to deliver on our promises. Only in this way, will we receive the support and funding we need.

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... [58-107] AI for the advancement of neuroscience 50 AI for neurological disorders 57 [165][166][167][168][169][170][171][172][173][174][175][176][177][178][179][180][181][182] Challenges and future directions of research 18 ...
... It has to adopt large-scale collaborations so that efficient resources and competencies from different teams of people can be acquired. Neuroscience has to shift from the present 'small-scale' working culture to large-scale teams involving experts from different domains [165]. Though neuroscience has been growing for many decades, different working teams focus on their concerned tasks in isolation. ...
... Though neuroscience has been growing for many decades, different working teams focus on their concerned tasks in isolation. As discussed in [165], geneticists work with mice, teams working on neural microcircuitry work with rats, teams working on the visual cortex work with cats, teams working on high cognition work with monkeys and human volunteers, etc. Here, the key point is that despite the diverse teams working on different aspects, the teams are in isolation. ...
Article
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Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.
... Interestingly, since the 1990s (known in the United States as the Decade of the Brain), interest in and the pursuit of knowledge in this field have only seemed to grow (OECD, 2002;. According to PubMed, in the mid 1960's, an average of 3,000 articles including the word "brain" were published per year; in 2019, this number increased to 94,615 (Markram, 2013;Fan and Markram, 2019;Tokuhama-Espinosa, 2019). ...
... A study conducted by the Brazilian Institute for Geography and Statistics (Instituto Brasileiro de Geografia e Estatística, IBGE) in 2018 revealed that internet use is highest among 18-29-year-olds (90-91%), and lowest among individuals 60 and older (38.7%), with steadily declining numbers as age increases (IBGE, 2018). Thus, given that much information (accurate or inaccurate) is obtained from the internet (Markram, 2013), and internet use is not equal across age groups, we questioned whether performance would also vary among the age groups tested. ...
... Thus, while we obtained responses from a large sample of Brazilians from all five geographical regions, age groups and several different professions, our sample does not represent the 11 million Brazilians over the age of 15 who are illiterate (EBC, 2020). Also, internet use is not the same across regions: a study from 2018 by the Brazilian Institute for Geography and Statistics (IBGE) found interregional differences in internet use (81.1% and 78.2% of people living in the Southeast and South use the internet, compared with 64.7% and 64% of people in the North and Northeast, respectively) (Markram, 2013). Our study also requires respondents to be interested in the topic and be motivated to respond, as all answers were voluntary. ...
Book
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En los últimos años se ha visto un creciente interés por el conocimiento relacionado con el cerebro y las neurociencias. Esto ha llevado a que se genere una importante cantidad de investigaciones y que el contexto propicie el surgimiento de creencias erróneas. Estudios realizados en varios países convergen en el hallazgo de que el conocimiento sobre neurociencias en todos los campos de conocimiento es pobre y en algunos estudios en Europa y América del Sur, incluso se observó que un mayor interés en neurociencia predice (paradójicamente) una mayor creencia en neuromitos, combinada con una incapacidad para juzgar información como real o pseudocientífica. La brecha entre la neurociencia cognitiva y el aprendizaje sigue siendo muy amplia . Y una de las consecuencias de esta distancia es la propagación de mitos que en muchos casos cuentan con algún sustento científico pero que son resultado de una malinterpretación o descontextualización de los resultados de investigaciones. Especialmente en el ámbito de la educación, la necesidad de incorporar recursos que permitan renovar la visión del aprendizaje ha favorecido el desarrollo de estas creencias erróneas, que se convierten en dogma y generan confusión sobre los aquellos aspectos que tienen una base científica y aquellos que deben ser refutados.
... Interestingly, since the 1990s (known in the United States as the Decade of the Brain), interest in and the pursuit of knowledge in this field have only seemed to grow (OECD, 2002;Dekker et al., 2012). According to PubMed, in the mid 1960's, an average of 3,000 articles including the word "brain" were published per year; in 2019, this number increased to 94,615 (Markram, 2013;Fan and Markram, 2019;Tokuhama-Espinosa, 2019). ...
... A study conducted by the Brazilian Institute for Geography and Statistics (Instituto Brasileiro de Geografia e Estatística, IBGE) in 2018 revealed that internet use is highest among 18-29-year-olds (90-91%), and lowest among individuals 60 and older (38.7%), with steadily declining numbers as age increases (IBGE, 2018). Thus, given that much information (accurate or inaccurate) is obtained from the internet (Markram, 2013), and internet use is not equal across age groups, we questioned whether performance would also vary among the age groups tested. ...
... Thus, while we obtained responses from a large sample of Brazilians from all five geographical regions, age groups and several different professions, our sample does not represent the 11 million Brazilians over the age of 15 who are illiterate (EBC, 2020). Also, internet use is not the same across regions: a study from 2018 by the Brazilian Institute for Geography and Statistics (IBGE) found interregional differences in internet use (81.1% and 78.2% of people living in the Southeast and South use the internet, compared with 64.7% and 64% of people in the North and Northeast, respectively) (Markram, 2013). Our study also requires respondents to be interested in the topic and be motivated to respond, as all answers were voluntary. ...
Article
Full-text available
The field of Neuroscience has experienced a growing interest in recent decades, which has led to an exponential growth in the amount of related information made available online as well as the market for Neuroscience-related courses. While this type of knowledge can be greatly beneficial to people working in science, health and education, it can also benefit individuals in other areas. For example, neuroscience knowledge can help people from all fields better understand and critique information about new discoveries or products, and even make better education- and health-related decisions. Online platforms are fertile ground for the creation and spread of fake information, including misrepresentations of scientific knowledge or new discoveries (e.g., neuromyths). These types of false information, once spread, can be difficult to tear down and may have widespread negative effects. For example, even scientists are less likely to access retractions of peer-reviewed articles than the original discredited articles. In this study we surveyed general knowledge about neuroscience and the brain among volunteers in Brazil, Latin America’s largest country. We were interested in evaluating the prevalence of neuromyths in this region, and test whether knowledge/neuromyth endorsement differs by age, region, and/or profession. To that end, we created a 30-item survey that was anonymously answered online by 1128 individuals. While younger people (20–29-year-olds) generally responded more accurately than people 60 and older, people in the North responded significantly worse than those in the South and Southeast. Most interestingly, people in the biological sciences consistently responded best, but people in the health sciences responded no better than people in the exact sciences or humanities. Furthermore, years of schooling did not correlate with performance, suggesting that quantity may surpass quality when it comes to extension or graduate-level course offerings. We discuss how our findings can help guide efforts toward improving access to quality information and training in the region.
... Today, for the first time, modern ICT has brought these goals within sight." Markram, 2013). The Flagship initiative was developed following a decision that Europe should reinforce its support for FET research under the ICT theme, to stimulate and explore new forms of multidisciplinary research collaboration going beyond existing organizational structures and models and reinforce its capability for permanent foresight of future research trends in ICT. ...
... From the start, it became obvious that the "Blue Sky" objective, the full simulation of the Human brain, was too high in the sky and would be difficult to achieve. Even within the HBP ramp-up phase, the project deliverables were quickly transformed to target building appropriate IT infrastructures and conceptualizing an optimal tool for brain-explorers: the microscope-telescope or "neuroscope" (Markram, 2013). ...
... It is going to be like a massive telescope or an MRI machine sitting in a hospital, and scientists will get together to write a proposal and they'll book half a day on the machine to run a simulation to test a particular hypothesis (Markram, 2011)." Progress is thus expected mostly away from the experimental bench, and gained from the alliance of deep learning, neuroinformatics and neuromorphic computation, promised to be significant enough to sustain virtual medicine applications (Markram, 2013;Sanz-Leon et al., 2013). ...
Article
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The recent trend toward an industrialization of brain exploration and the technological prowess of artificial intelligence algorithms and high-performance computing has caught the imagination of the public. These impressive advances are fueling an uncontrolled societal hype, the more amplified, the more "Blue Sky" the claim is. Will we ever be able to simulate a brain in silico? Will "it" (the digital avatar) be conscious? The Blue Brain Project (BBP) and the European flagship the Human Brain Project (HBP) have surfed on this wave for the past 10 years. Their already significant lifetimes now offer new case studies for neuroscience sociology and epistemology, as the projects mature. Their distinctive "Blue Sky" flavor has been a key feature in securing unprecedented funding (more than one billion Euros) mostly through supranational institutions. The longitudinal analysis of these ventures provides clues to how the neuromyth they propagate sells science, in a scientific world based on an economy of promises.
... The field of neuroscience has experienced a considerable growth in recent years [1]. The ultimate goal of the disciplines included under the neuroscience term is the brain function analysis and understanding, in health and disease, to discover the best therapies for brain disorders [2]. ...
... In addition, we created a NF-sham group with the last 20 volunteers who were not aware of their placebo condition and who, as the selected group did, always chose the type and form of the reinforcer. (1). Their sensorimotor rhythm (SMR) is calculated in real time (2). ...
... If their SMR goes above the configured threshold, then they get to see the image (3); if they do not get their SMR above the threshold or it falls below the threshold, then the screen dimmers preventing the subject from watching the movie (4). (1). Their sensorimotor rhythm (SMR) is calculated in real time (2). ...
Article
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The brain activity that is measured by electroencephalography (EEG) can be modified through operant conditioning, specifically using neurofeedback (NF). NF has been applied to several disorders claiming that a change in the erratic brain activity would be accompanied by a reduction of the symptoms. However, the expected results are not always achieved. Some authors have suggested that the lack of an adequate response may be due to an incorrect application of the operant conditioning principles. A key factor in operant conditioning is the use of reinforcers and their value in modifying behavior, something that is not always sufficiently taken into account. This work aims to clarify the relevance of the motivational value versus the purely informational value of the reinforcer. In this study, 113 subjects were randomly assigned two different reinforcer conditions: a selected reinforcer—the subjects subjectively selected the reinforcers—or an imposed reinforcer— the reinforcers were assigned by the experimenter—and both groups undertook NF sessions to enhance the sensorimotor rhythm (SMR). In addition, the selected reinforcer group was divided into two subgroups: one receiving real NF and the other one sham NF. There were no significant differences between the groups at baseline in terms of SMR amplitude. After the intervention, only those subjects belonging to the selected reinforcer group and receiving real NF increased their SMR. Our results provide evidence for the importance of the motivational value of the reinforcer in Neurofeedback success.
... Over the last two decades, neuroscience has experienced a major growth. This growth, along with a set of main objectives of this field of knowledge, are presented in [1]. As a consequence, neuromorphic engineering, which was a concept presented by Carver Mead in [2] that focuses on the study, design and implementation of hardware and software with the aim of mimicking the basic principles of biological nervous systems, has become one of the most promising scientific fields. ...
... Thus, this is the closest approach of neural networks to biological functioning [19]. 1 https://github.com/alvayus/sPyBlocks/ Information is transmitted across synapses in the form of spikes, which are asynchronous electric pulses (large peaks in the membrane potential of neurons that occur when the membrane potential reaches the threshold potential) produced by neurons. ...
Preprint
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One of the most interesting and still growing scientific fields is neuromorphic engineering, which is focused on studying and designing hardware and software with the purpose of mimicking the basic principles of biological nervous systems. Currently, there are many research groups developing practical applications based on neuroscientific knowledge. This work provides researchers with a novel toolkit of building blocks based on Spiking Neural Networks that emulate the behavior of different logic gates. These could be very useful in many spike-based applications, since logic gates are the basis of digital circuits. The designs and models proposed are presented and implemented on a SpiNNaker hardware platform. Different experiments were performed in order to validate the expected behavior, and the obtained results are discussed. The functionality of traditional logic gates and the proposed blocks is studied, and the feasibility of the presented approach is discussed.
... Recent advancements in the field particularly regarding the use of artificial intelligence networks from research such as the Blue Brian Project and the Human Brain project make clear that such processes are complex and that complex learning is an emergent process (Kanari, Ramaswamy, Shi, Morand, Meystre et. al., 2019;Markram, 2013). This gives weight to both Allport and Luria's assertion that, regarding the human species, certain processes are more usefully examined at this intraindividual level. ...
... The degree of complexity of brain processes is currently being examined using AI simulations (Kanari, Ramaswamy, Shi, Morand, Meystre et. al., 2019;Markram, 2013). Other studies establish that the brain is subject to external stimuli well into adulthood and remains plastic throughout lifespan (Blakemore, 2012;Blakemore & Choudhury, 2006;Boldrini, Fulmore, Tartt, Simeon, Pavlova et. ...
Thesis
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The dynamic, multifaceted nature of humans requires an individuated, dynamic approach to evaluation and intervention. The primary purpose of this research is to address issues of evaluating dynamic assessment research and practice. In order to usefully consider a methodology of measurement which aligns with the philosophical foundations of DA it was necessary to propose a widening of the parameters or scope of reference within which DA is situated. The situation of DA within a copasetic framework – Integrated Social Learning Theory (ISLT) clarifies the theoretical basis for research and practice. The novel idiographic methodology developed for this thesis Individual Dynamic Evaluation and Assessment (IDEA) uses open card-sorts to capture the participant’s self-concept. Multidimensional scaling analysis of card-sort data renders a graphical representation of that self-concept in relation to others in the form of a life-space map. General Procrustes analysis of these life-space maps over time allows the evaluation of movement in self-concept for a person over time. DA is primarily concerned with the mediation of learning between the expert and novice. The focus of DA is the person, and the examination of movement or change for that person. Drawing from development and social learning theories which align with this position bolsters the grand theories of dynamic DA posited by Vygotsky, Luria (Luria, 1976; Luria & Cole, 1976; Luria, Cole & Cole, 2006; Luria & Yudovich, 1956, 1959), Haeussermann (1956), Feuerstein (1990, 2003; Feuerstein, Rand & Hoffmann, 1979; Feuerstein, Feuerstein, Falik & Rand, 2002 ) Bruner (1956, 1960) & Rey (1938). The ISLT framework allows for the useful consideration of intraindividual methods of evaluation and measurement. A position has been taken – namely that nomothetic methods of measurement are not best suited to the goal of usefully examining change over time in therapeutic practice contexts. ISLT and IDEA-1 consider the person as a complex, dynamic system. Learning and psychological support are inextricably linked within this paradigm. This has ramifications for practice. A holistic approach to psycho-educational support is recommended, the basis for which is provided within the ISLT framework. This thesis presents a novel N=1 case study design which is wholly idiographic in nature. The methodology for evaluation described here provides a basis for evidence-based practice while maintaining a focus on the progress of the individual under targeted intervention. The repeated measures design described here is one which has a format with which practitioners and researchers are familiar. It stands separate from the intervention procedure unlike integrated scoring systems and is idiographic in focus unlike nomothetic sandwich study designs. The results from the sixteen studies presented here provides the beginnings of an evidence-base for the use of this approach in intraindividual contexts.
... Brain activity has been demonstrated to possess significant multiscale variability [11], pronounced nonstationarity [12], nondifferentiability, long-range spatiotemporal dependence, and fractal behavior that resembles to fractional dynamics [13], [14]. This complexity makes the development of mathematical modeling [8], [9], [13], [15] and predictive tools of human brain dynamics a challenging task [3], [4], [10], [11]. Starting from these premises, recent efforts are focusing on analyzing the nonstationary fractal behavior of brain activity and determining the sensing requirements [14], [15] within a cyber-physical systems approach. ...
... Understanding how information is physically stored in the brain, how cognitive function arises from brain activity and interaction with sensing modalities or how psychiatric episodes or disease emergence in human behavior require rigorous mathematical tools and efficient computing architectures to mine the complex spatiotemporal fluctuations of brain web of signals [e.g., electroencephalogram (EEG) and blood-oxygenlevel dependent]. Toward this end, there is a great need for developing novel hardware and software capable of simulating the brain [10] or stimulating the human brain to avoid disease states [4], [16]. Such highly efficient supercomputing architectures [1] must rely on advanced dynamic causal mathematical modeling paradigms [8], [13] and must be able to mine large collections of brain signals in real time, translate physiological dynamics into accurate perception, abstraction/knowledge, and decode the human optimal decisions under significant uncertainty. ...
Article
Understanding the dynamics and functionality of the human brain and its relationship with different physical entities has proven to be extremely useful in many applications including disability therapy and designing the next-generation user-interfaces. Communication between the brain and external hardware using neural stimulation and recordings has also been demonstrated recently. Such systems are usually analyzed by employing the brain-machine-body interface (BMBI) model. However, owing to the high complexity of the human brain activity, modeling and analyzing the neural-signals is a resource-intensive task. Moreover, coupling neural signals from different physical entities inevitably leads to large input data sets and hence, also making it data- and computationally intensive. Hence, here we employ a spatiotemporal fractal parallel algorithm to efficiently generate and analyze the BMBI models. However, such an algorithm can lead to demanding on-chip traffic patterns requiring an efficient communication infrastructure among different computing cores. To address this issue, we propose a machine-learning-inspired wireless network-on-chip (WiNoC)-based manycore architecture for handling the compute- and communication-intensive nature of the BMBI applications. The experimental results show that, compared with the traditional wireline mesh NoC, WiNoC achieves up to 55% savings in energy delay product for a system size of 1024 cores.
... Historicamente, a visão do cérebro evoluiu conforme os métodos científicos avançaram. No século XIX, por exemplo, estudos sobre pacientes com lesões cerebrais sugeriram que o cérebro era composto por centros especializados, como a "Área de Broca", responsável pela produção da fala (Markram, 2013). Já no século XX, a partir do desenvolvimento de técnicas de registro neurofisiológico, foi criada a "doutrina do neurônio" de Barlow, que destacou o neurônio como a unidade funcional e estrutural do sistema nervoso. ...
Article
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O uso de células-tronco pluripotentes induzidas (iPSCs) na neurociência do século 21 apresenta promessas e desafios significativos. Essas células, reprogramadas a partir de células somáticas adultas, têm o potencial de modelar doenças neurodegenerativas, como Alzheimer, Parkinson e Esclerose Lateral Amiotrófica, permitindo uma compreensão mais profunda dos mecanismos patológicos subjacentes. Além disso, as iPSCs oferecem oportunidades para o desenvolvimento de novas terapias, incluindo a triagem de medicamentos e a terapia celular, visando corrigir defeitos genéticos e restaurar a função neuronal. No entanto, o uso de iPSCs na neurociência enfrenta diversos desafios técnicos, científicos e éticos. A diferenciação eficiente das iPSCs em tipos celulares específicos do sistema nervoso central, a reprodutibilidade dos resultados e a garantia de segurança das terapias baseadas em iPSCs são algumas das questões críticas a serem abordadas. Além disso, preocupações éticas relacionadas à origem das células e à manipulação genética devem ser cuidadosamente consideradas. Apesar desses desafios, avanços significativos têm sido feitos na criação de modelos celulares mais sofisticados, como organoides cerebrais, que recapitulam características complexas do cérebro humano em desenvolvimento e em doenças. Integração de abordagens multidisciplinares, como inteligência artificial e big data, também pode oferecer insights valiosos para avançar na compreensão e tratamento de doenças neurológicas. Em suma, as iPSCs representam uma ferramenta poderosa na neurociência moderna, oferecendo novas oportunidades para elucidar os mecanismos das doenças neurodegenerativas e desenvolver terapias mais eficazes. No entanto, é necessário um esforço contínuo para superar os desafios técnicos, científicos e éticos associados ao seu uso.
... Since NeuroIS research heavily draws on neurophysiological data to test hypotheses, a substantial number of participants is required for each experiment [1,5]. However, it is often difficult to find the required number of participants that meet sampling criteria [6]. At the same time, neurophysiological methods require a joint analysis of environmental stimuli, the neural system, and bodily reactions, which is why virtual models cannot easily replace participants. ...
... After more than a century of extensive research in anatomy and physiology we understand anatomical connectivity better -but have made limited progress in understanding function, especially at the whole-organism scale, including how multi-scale dynamical interactions within the central nervous system (CNS) give rise to our behaviors and phenomenal experiences [4]. Our limited progress may be due to avoiding difficult paradigms involving behavior [5,6], being disconnected from psychology [7], or lacking large datasets and powerful models [8]. Recent controversies including possibly pursuing the wrong approach in Alzheimer's research for decades [9] raise the question whether seeking to provide completely mechanistic models of brain function will ever succeed at the whole organisms level, help solve complex diseases or explain capacities such as consciousness. ...
Preprint
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A central goal in neuroscience is to provide explanations for how animal nervous systems can generate actions and cognitive states such as consciousness while artificial intelligence (AI) and machine learning (ML) seek to provide models that are increasingly better at prediction. Despite many decades of research we have made limited progress on providing neuroscience explanations yet there is an increased use of AI and ML methods in neuroscience for prediction of behavior and even cognitive states. Here we propose emulator theory (ET) and neural emulators as circuit- and scale-independent predictive models of biological brain activity and emulator theory (ET) as an alternative research paradigm in neuroscience. ET proposes that predictive models trained solely on neural dynamics and behaviors can generate functionally indistinguishable systems from their sources. That is, compared to the biological organisms which they model, emulators may achieve indistinguishable behavior and cognitive states - including consciousness - without any mechanistic explanations. We posit ET via several conjectures, discuss the nature of endogenous and exogenous activation of neural circuits, and discuss neural causality of phenomenal states. ET provides the conceptual and empirical framework for prediction-based models of neural dynamics and behavior without explicit representations of idiosyncratically evolved nervous systems.
... Since NeuroIS research heavily draws on neurophysiological data to test hypotheses, a substantial number of participants is required for each experiment [1,5]. However, it is often difficult to find the required number of participants that meet sampling criteria [6]. At the same time, neurophysiological methods require a joint analysis of environmental stimuli, the neural system, and bodily reactions, which is why virtual models cannot easily replace participants. ...
Conference Paper
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Cognitive Robotics aims to develop robots that can perform tasks, learn from experiences, and adapt to new situations using cognitive skills. Rooted in neuroscience theories, Cognitive Robotics provides a unique opportunity for NeuroIS researchers to theorize and imagine intelligent autonomous agents as natural cognitive systems. By translating Cognitive Robotics methods and architectures into the NeuroIS into the 2x2 design science research matrix, we intend to help researchers gain deeper insights into how humans perceive and interact with their environment. These insights may not only improve cognitive architectures but may also enable a better design and evaluation of user-centric NeuroIS systems, safer test propositions, and better self-adaptable systems that can effectively collaborate with humans in various settings.
... Flagships were meant to be blue-sky projects, revolutionizing conceptual knowledge, addressing challenges thought to be at the limits of feasibility at the time, and generating disruptive technologies. In this context, the priority for HBP was to enforce a paradigm shift (Kuhn, 1962), that would revolutionize the way we look at the brain in terms of science and applications (Markram, 2006(Markram, , 2012(Markram, , 2013Kandel et al., 2013). Note here that, in the context of the "blue sky" framing, delivering what had been promised was a different issue, and probably, in most minds, not mandatory. ...
... We have to set goals for ourselves that the public can recognize and share. 5 Within these challenges we can clearly identify that except the second one (ii) the rest can be conceptualized as methodological issues (the need of technical innovation). The second challenge would be related to an unsolved question of neuroscience, this question would revolve around the explanation of the structure (and eventually the activity) of the nervous system. ...
Article
Desde el origen de la neurociencia como disciplina se han realizado grandes descubrimientos en esta materia pero al mismo tiempo surgen interrogantes y problemas que esta ciencia todavía no puede resolver. Probablemente no exista consenso al momento de definir cuáles son los unsolved problems de la neurociencia, pero sobre lo que sí se está seguro es en reconocer que estos problemas existen. ¿La neurociencia del futuro, con sus logros y sus unsolved problems, es la neurociencia que queremos? En el presente documento se elaborará un recuento de los llamados unsolved problems de la neurociencia de acuerdo (según) varios autores que han analizado a futuro los límites de esta disciplina. Asimismo, se realizará una sumarización del itinerario de ideas de la Teoría Sociobiológica Informacional de Pedro Ortiz Cabanillas (1933-2011) como propuesta alternativa para así finalmente se perfile la neurociencia del futuro, la neurociencia que deseamos.
... In this subsection, we showed the rationale for the use of computational models in the analysis of central nervous system processes, including the physiopathology of burnout. Computational models begin the predominant way of linking current theoretical concepts to results of experimental studies, even though they are not obvious or hidden.Many researchers think that there is no further progress in, e.g., medicine or psychology, without bioinformatics, biocybernetics, and healthcare informatics [11,12]. Fuzzy medical knowledge can be used to model uncertainty and ambiguity in medical concepts and their sets. ...
Article
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Occupational burnout, manifested by emotional exhaustion, lack of a sense of personal achievement, and depersonalization, is not a new phenomenon, but thusfar, there is no clear definition or diagnostic guidelines. The aim of this article wasto summarize all empirical studies to date that have used medical neuroimaging techniques to provide evidence or links regarding changes in brain function in occupational burnout syndrome from a neuroscientific perspective, and then use these to propose a fuzzy-based computational model of burnout.A comprehensive literature search was conducted in two major databases (PubMed and Medline Complete). The search period was 2006–2021, and searches were limited to the English language. Each article was carefully reviewed and appropriately selected on the basis of raw data, validity of methods used, clarity of results, and scales for measuring burnout. The results showed that the brain structures of patients with job burnout that are associated with emotion, motivation, and empathy weresignificantly different from healthy controls. These altered brain regions included the thalamus, hippocampus, amygdala, caudate, striatum, dorso-lateral prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, anterior insula, inferior frontal cingulate cortex, middle frontal cingulate cortex, temporoparietal junction, and grey matter. Deepening our understanding of how these brain structures are related to burnout will pave the way for better approaches fordiagnosis and intervention. As an alternative to the neuroimaging approach, the paper presents a late proposal of the PLUS (personal living usual satisfaction) parameter. It is based on a fuzzy model, wherein the data source is psychological factors—the same or similar to the neuroimaging approach. As the novel approach to searching for neural burnout mechanisms, we have shown that computational models, including those based on fuzzy logic and artificial neural networks, can play an important role in inferring and predicting burnout. Effective computational models of burnout are possible but need further development to ensure accuracy across different populations. There is also a need to identify mechanisms and clinical indicators of chronic fatigue syndrome, stress, burnout, and natural cognitive changes associated with, for example, ageing, in order to introduce more effective differential diagnosis and screening.
... Technological advances in recent decades have made neuroscience and its related fields one of the fastest growing areas of research. According to PubMed, an average of 3000 articles with the word "brain" were published per year in the mid-1960s [1], and in 2019, this number reached over 94,000. In the United States, 1990-2000 was called the Decade of the Brain, and although two decades have passed, interest in and the pursuit of knowledge in this field have not seemed to decrease. ...
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The field of neuroscience has seen significant growth and interest in recent decades. While neuroscience knowledge can benefit laypeople as well as professionals in many different areas, it may be particularly relevant for educators. With the right information, educators can apply neuroscience-based teaching strategies as well as protect themselves and their students against pseudoscientific ideas and products based on them. Despite rapidly growing sources of available information and courses, studies show that educators in many countries have poor knowledge of brain science and tend to endorse education-related neuromyths. Poor English skills and fewer resources (personal, institutional and governmental) may be additional limitations in Latin America. In order to better understand the scenario in Latin America’s largest country, we created an anonymous online survey which was answered by 1634 individuals working in education from all five regions of Brazil. Respondents stated whether they agreed with each statement and reported their level of confidence for each answer. Significant differences in performance were observed across regions, between educators living in capital cities versus the outskirts, between those teaching in private versus public schools, and among educators teaching different levels (pre-school up to college/university). We also observed high endorsement of some key neuromyths, even among groups who performed better overall. To the best of our knowledge, this is the first study to conduct a detailed analysis of the profile of a large group of educators in Brazil. We discuss our findings in terms of efforts to better understand regional and global limitations and develop methods of addressing these most efficiently.
... There exists no strict correlation between the amount of brain tissue lost and the extent of the impact on behavior. Apart from the enormous complexity, our limited understanding is a result of not being able to directly manipulate the living human brain to a great extent and extrapolating results from other organisms or simulations (Markram, 2013). The exact process in which increased or decreased brain tissue of any type leads to decreased executive control and eventually to a violent outburst is unclear. ...
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Neuroscience can provide evidence in some cases of legal matters, despite its tenuous nature. Among others, arguing for diminished capacity, insanity, or pleading for mitigation is the most frequent use of neurological evidence in the courtroom. While there is a plethora of studies discussing the moral and legal matters of the practice, there is a lack of studies examining specific cases and the subsequent applications of brain knowledge. This study details the capital punishment trial of Kelvin Lee Coleman Jr., charged in 2013 with double murder in Tampa, Florida, to illustrate the extent that expert opinions – based on neuroimaging, neurological, and neuropsychiatric examinations – had an impact on the court’s decisions. The defendant was sentenced to life imprisonment without the possibility of parole. According to the comments of the trial’s jury, the most influential reason for not sentencing the defendant to death is the fact that during the incident was that he was under extreme mental and emotional disturbance. Other reasons were evidence of brain abnormalities resulting from neurological insult, fetal alcohol syndrome, and orbitofrontal syndrome contributing to severely abnormal behavior and lack of impulse control.
... We however found relatively small variations in electrode impedance with varying SU-8 thickness. The trend in our simulated impedances indicates decreasing impedance with increasing ESA, which is consistent with trends reported in literature 24 .Furthermore, our simulated impedances are consistent with values reported for commercially available 3D electrodes in 0.9% saline, which validated the accuracy of our simulation 3,4,25 . ...
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Neural recordings made to date through various approaches—both in-vitro or in-vivo—lack high spatial resolution and a high signal-to-noise ratio (SNR) required for detailed understanding of brain function, synaptic plasticity, and dysfunction. These shortcomings in turn deter the ability to further design diagnostic, therapeutic strategies and the fabrication of neuro-modulatory devices with various feedback loop systems. We report here on the simulation and fabrication of fully configurable neural micro-electrodes that can be used for both in vitro and in vivo applications, with three-dimensional semi-insulated structures patterned onto custom, fine-pitch, high density arrays. These microelectrodes were interfaced with isolated brain slices as well as implanted in brains of freely behaving rats to demonstrate their ability to maintain a high SNR. Moreover, the electrodes enabled the detection of epileptiform events and high frequency oscillations in an epilepsy model thus offering a diagnostic potential for neurological disorders such as epilepsy. These microelectrodes provide unique opportunities to study brain activity under normal and various pathological conditions, both in-vivo and in in-vitro, thus furthering the ability to develop drug screening and neuromodulation systems that could accurately record and map the activity of large neural networks over an extended time period.
... Blue Brain focused on exploiting models from information and communication technology (ICT) to simulate a human brain. Largely complete [15], the Blue Brain project assets have been folded in to the HBP. That enterprise will encompass ten years of effort by a consortium of 113 academic partners in Europe, Israel, and the Western Hemisphere. ...
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There appear to be scientific, medical, commercial, political, and patient stakeholder communities cooperating in a drug discovery and development ecosystem. Although a mundane concept, this essay intends to elaborate on the structure, function and values of the communities constituting that system. The goal is to present to the reader a snapshot of the drug discovery and development ecosystem and its surroundings, and to highlight internal and external factors said to be in control of the ecosystem dynamic. The principal thesis to be explored is that that "community" is a convenient intellectual shorthand for mass action observed after the fact. The thesis will be supported by empirical evidence that it is choices made by individual community members which determine the course, heading, and speed of the drug discovery and development ecosystem. This goal of this essay is to stimulate reflection and conversation about and within stakeholder communities as identified here.
... Neuroscience is among the most intellectually demanding of the basic science disciplines (Markram, 2013) in the first two years of medical education, whether the instruction is presented in-person or online. The expectations of the same level of excellence prevented diluting the material even though it was presented on line. ...
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The implementation of an integrated medical neuroscience course by technologically pivoting an in‐person neuroscience course to online using an adaptive blended method may provide a unique approach for teaching a medical neuroscience course during the Covid‐19 pandemic. An adaptive blended learning method was developed in response to the requirements necessitated by the Covid‐19 pandemic. This model combined pedagogical needs with digital technology using online learning activities to implement student learning in a medical neuroscience course for year one medical students. This approach provided medical students with an individually customized learning opportunity in medical neuroscience. The students had the complete choice to engage the learning system synchronously or asynchronously and learn neuroscience materials at different locations and times in response to the demands required to deal with the pandemic. Students' performance in summative and formative examinations of the adaptive blended learning activities were compared with the previous performance obtained the previous year when the contents of the medical neuroscience course were implemented using the conventional “face‐to‐face” learning approach. While the cohort of our students in 2019 and 2020 changed, the contents, sessions, volume of material, and assessment were constant. This enabled us to compare the results of the 2019 and 2020 classes. Overall, students' performance was not significantly different between the adaptive blended learning and the in‐person approach. More students scored between 70% and 79% during the adaptive blended learning compared with in‐class teaching, while more students scored between 80% and 89% during the in‐person learning than during the adaptive blended learning. Finally, the percentage of students that scored >90% was not significantly different for both Years 2019 and 2020. The adaptive blended learning approach was effective in enhancing academic performance for high‐performing medical students. It also permitted the early identification of underachieving students, thereby serving as an early warning sign to permit timely intervention.
... More recent large-scale efforts include the Human Brain Project systems, SpiNNaker [2], and BrainScaleS [3], which were commissioned for the specific purpose of accelerating neuroscience simulations [4]. Despite promising quantitative experiments [5], [6], these systems have struggled to demonstrate value as a practical tool for neuroscience discovery [7]. ...
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Deep artificial neural networks apply principles of the brain's information processing that led to breakthroughs in machine learning spanning many problem domains. Neuromorphic computing aims to take this a step further to chips more directly inspired by the form and function of biological neural circuits, so they can process new knowledge, adapt, behave, and learn in real time at low power levels. Despite several decades of research, until recently, very few published results have shown that today's neuromorphic chips can demonstrate quantitative computational value. This is now changing with the advent of Intel's Loihi, a neuromorphic research processor designed to support a broad range of spiking neural networks with sufficient scale, performance, and features to deliver competitive results compared to state-of-the-art contemporary computing architectures. This survey reviews results that are obtained to date with Loihi across the major algorithmic domains under study, including deep learning approaches and novel approaches that aim to more directly harness the key features of spike-based neuromorphic hardware. While conventional feedforward deep neural networks show modest if any benefit on Loihi, more brain-inspired networks using recurrence, precise spike-timing relationships, synaptic plasticity, stochasticity, and sparsity perform certain computation with orders of magnitude lower latency and energy compared to state-of-the-art conventional approaches. These compelling neuromorphic networks solve a diverse range of problems representative of brain-like computation, such as event-based data processing, adaptive control, constrained optimization, sparse feature regression, and graph search.
... Evidences for stone age cranial surgery around 7000 years ago (Alt et al., 1997) could indicate that this question has been of concern to mankind for a long time. Still, in the 21th century, there are many challenges in neuroscience (Markram, 2013) and the underlying mechanisms of basic brain functions are incompletely understood (Rees et al., 2002). ...
Thesis
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The goal of this doctoral thesis is to identify appropriate methods for the estimation of connectivity and for measuring synchrony between spike trains from in vitro neuronal networks. Special focus is set on the parameter optimization, the suitability for massively parallel spike trains, and the consideration of the characteristics of real recordings. Two new methods were developed in the course of the optimization which outperformed other methods from the literature. The first method “Total spiking probability edges” (TSPE) estimates the effective connectivity of two spike trains, based on the cross-correlation and a subsequent analysis of the cross-correlogram. In addition to the estimation of the synaptic weight, a distinction between excitatory and inhibitory connections is possible. Compared to other methods, simulated neuronal networks could be estimated with higher accuracy, while being suitable for the analysis of massively parallel spike trains. The second method “Spike-contrast” measures the synchrony of parallel spike trains with the advantage of automatically optimizing its time scale to the data. In contrast to other methods, which also adapt to the characteristics of the data, Spike-contrast is more robust to erroneous spike trains and significantly faster for large amounts of parallel spike trains. Moreover, a synchrony curve as a function of the time scale is generated by Spike-contrast. This optimization curve is a novel feature for the analysis of parallel spike trains.
... The design and realization of higher-order electronic elements will enable extremely efficient implementations of neuromorphic artificial intelligence. Such realizations may also provide a platform on which to explore models of higher-order brain functions (for example, psychiatric conditions), which are currently impeded by computing bottlenecks 16,17 . ...
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Current hardware approaches to biomimetic or neuromorphic artificial intelligence rely on elaborate transistor circuits to simulate biological functions. However, these can instead be more faithfully emulated by higher-order circuit elements that naturally express neuromorphic nonlinear dynamics1–4. Generating neuromorphic action potentials in a circuit element theoretically requires a minimum of third-order complexity (for example, three dynamical electrophysical processes)⁵, but there have been few examples of second-order neuromorphic elements, and no previous demonstration of any isolated third-order element6–8. Using both experiments and modelling, here we show how multiple electrophysical processes—including Mott transition dynamics—form a nanoscale third-order circuit element. We demonstrate simple transistorless networks of third-order elements that perform Boolean operations and find analogue solutions to a computationally hard graph-partitioning problem. This work paves a way towards very compact and densely functional neuromorphic computing primitives, and energy-efficient validation of neuroscientific models.
... All these examples show an emerging interest in neuroscience projects in countries across the developed countries in the world. While neuroscience is a major scientific undertaking in the 21st century, advances in basic research have not yet translated into benefits for society (Markram, 2013). ...
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This chapter discusses the opportunities and challenges involved in combining the two fields of neuroscience and talent management (often abbreviated as TM), starting from the assumption that the need to merge them is justified by their complementarities, rather than by the level of analysis they focus on. The authors discuss potential benefits and drawbacks for management research using methods obtained from cognitive neuroscience. Firstly, they discuss distinct advantages in applying techniques allowing researchers to track processes that are essential to the talent management field, warning that neuroscientific approaches and technologies are not commonly used. Secondly, they define main problems, which describe the limits within which management scientists can usefully apply these approaches. Thirdly, they suggest a new perspective that incorporates the complementary capacities of managers and neuroscientists to generate useful information and perspective for both disciplines.
... Of all the subfields of medical research, neuroscience research is the most interesting and challenging one. 4 The term neuroscience encompasses research in the fields of clinical specialities such as neurology, neurosurgery, neuropsychiatry, and psychology as well as non-clinical disciplines such as neurobiology and neurochemistry. It also includes non-medical fields, including biomedical imaging, physics, computer science, and artificial intelligence. ...
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Objective: To review the dynamics of neuroscience research in the Kingdom of Saudi Arabia (KSA) from 2013-2018. Methods: Subject category of Neuroscience was selected in the SciVal feature of Scopus database, which includes all relevant categories of the field limiting it to Saudi Arabia. Results: Saudi Arabia is ranked 39th in publishing neuroscientific research worldwide. The number of yearly published articles has increased from 123 to 332 during the time period between 2013 and 2018. King Saud University & King Abdul Aziz University & their corresponding regions namely Western and Central regions are the major contributors to publications. Neuroscientists working in Saudi Arabia have collaboration with scientists from all over the world. The top 10 preferred journals are all international. In subcategories of neuroscience, developmental neuroscience seems the one that needs attention. Conclusion: Neuroscience research is on the rise in KSA. Older and well-established institutions like King Saud University & King Abdul Aziz University have taken lead in publishing neuroscientific research. International collaboration in all subfields of neuroscience is substantial. Eastern Southern and Northern regions and developmental neuroscience require more focus and funding.
... To be sure, this will not be an in depth investigation of the HBP but a form of neurocultural discourse analysis of statements in the public and scientific domain made by Henry Markram, director of HBP at the EPFL in Lausanne, and those of some of his colleagues and competitors. It will be limited to a number of papers by his hand (Markram 2006(Markram , 2011(Markram , 2012(Markram , 2013Markram et al. 2011) The critical neuroscience project is in fact very philosophical. The inaugural paper's bibliography abounds with references to philosophical canonical works like Foucault's Madness and Civilization, Lorraine Daston and Peter Galison's Objectivity, and Joseph Rouse's Engaging Science: How To Understand its Practices Philosophically. ...
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Sociologists of science claim there is a 'neurobiologization of society' going on. Advances in neuroscience would pose challenges to various societal domains and neurobiological reductionism could even threaten the humanistic legacy. On the other hand, new neurobiological insights may yield potential benefits for human health and education. Novel fields of study and business arise like neuroeducation and neuromarketing that try to 'link' neuroscience and society. A sociology of neuroscience starts to form, now that a growing number of scholars is analyzing these 'neurocultural' discourses. In parallel, many neuroscientists worry about how their research findings transform in the public domain, urging for clearer communication and reflective practice. This paper tries to complement the toolkit of critical neuroscience while redefining the so-called 'neuromyth' concept. Here, I attempt to reach a broader conceptualization departing from the conviction that the myth goes deeper than mere misapprehension in society.
... The causal relationship between components of the nervous system at different spatio-temporal scales, from subcellular mechanisms to behavior, still needs to be disclosed and this represents one of the main challenges of modern neuroscience. To this aim, bottom-up modeling is an advanced strategy that allows to propagate low-level cellular phenomena into large-scale brain networks (Markram, 2013;Markram et al., 2015;D'Angelo and Gandini Wheeler-Kingshott, 2017). ...
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Brain neurons exhibit complex electroresponsive properties – including intrinsic subthreshold oscillations and pacemaking, resonance and phase-reset – which are thought to play a critical role in controlling neural network dynamics. Although these properties emerge from detailed representations of molecular-level mechanisms in “realistic” models, they cannot usually be generated by simplified neuronal models (although these may show spike-frequency adaptation and bursting). We report here that this whole set of properties can be generated by the extended generalized leaky integrate-and-fire (E-GLIF) neuron model. E-GLIF derives from the GLIF model family and is therefore mono-compartmental, keeps the limited computational load typical of a linear low-dimensional system, admits analytical solutions and can be tuned through gradient-descent algorithms. Importantly, E-GLIF is designed to maintain a correspondence between model parameters and neuronal membrane mechanisms through a minimum set of equations. In order to test its potential, E-GLIF was used to model a specific neuron showing rich and complex electroresponsiveness, the cerebellar Golgi cell, and was validated against experimental electrophysiological data recorded from Golgi cells in acute cerebellar slices. During simulations, E-GLIF was activated by stimulus patterns, including current steps and synaptic inputs, identical to those used for the experiments. The results demonstrate that E-GLIF can reproduce the whole set of complex neuronal dynamics typical of these neurons – including intensity-frequency curves, spike-frequency adaptation, post-inhibitory rebound bursting, spontaneous subthreshold oscillations, resonance, and phase-reset – providing a new effective tool to investigate brain dynamics in large-scale simulations.
... Neuroscience is a fast growing field posing many challenges and requiring expertise in various different scientific areas (Markram, 2013). In order to be able to satisfy these needs the neuroscience community is asked to constantly develop new strategies for data analysis, design new experiments, improve techniques used in the past, identify previous errors and correct them etc. ...
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Low resolution electromagnetic tomography (LORETA) is a well-known method for the solution of the l2-based minimization problem for EEG/MEG source reconstruction. LORETA with a volume-based source space is widely used and much effort has been invested in the theory and the application of the method in an experimental context. However, it is especially interesting to use anatomical prior knowledge and constrain the LORETA's solution to the cortical surface. This strongly reduces the number of unknowns in the inverse approach. Unlike the Laplace operator in the volume case with a rectangular and regular grid, the mesh is triangulated and highly irregular in the surface case. Thus, it is not trivial to choose or construct a Laplace operator (termed Laplace-Beltrami operator when applied to surfaces) that has the desired properties and takes into account the geometry of the mesh. In this paper, the basic methodology behind cortical LORETA is discussed and the method is applied for source reconstruction of simulated data using different Laplace-Beltrami operators in the smoothing term. The results achieved with the different operators are compared with respect to their accuracy using various measures. Conclusions about the choice of an appropriate operator are deduced from the results.
... Now we come to the third research front, translational research in brain diseases. Citing a paper from Henry Markram [75], the director of the Blue Brain Project. I want to emphasis the importance of translational research in order to bring the knowledge we have into action against the diseases that affect the human brain. ...
Thesis
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Neurobiology is widely supported by bioinformatics. Due to the big amount of data generated from the biological side a computational approach is required. This thesis presents four different cases of bioinformatic tools applied to the service of Neurobiology. The first two tools presented belong to the field of image processing. In the first case, we make use of an algorithm based on the wavelet transformation to assess calcium activity events in cultured neurons. We designed an open source tool to assist neurobiology researchers in the analysis of calcium imaging videos. Such analysis is usually done manually which is time consuming and highly subjective. Our tool speeds up the work and offers the possibility of an unbiased detection of the calcium events. Even more important is that our algorithm not only detects the neuron spiking activity but also local spontaneous activity which is normally discarded because it is considered irrelevant. We showed that this activity is determinant in the calcium dynamics in neurons and it is involved in important functions like signal modulation and memory and learning. The second project is a segmentation task. In our case we are interested in segmenting the neuron nuclei in electron microscopy images of c.elegans. Marking these structures is necessary in order to reconstruct the connectome of the organism. C.elegans is a great study case due to the simplicity of its nervous system (only 502 neurons). This worm, despite its simplicity has taught us a lot about neuronal mechanisms. There is still a lot of information we can extract from the c.elegans, therein lies the importance of reconstructing its connectome. There is a current version of the c.elegans connectome but it was done by hand and on a single subject which leaves a big room for errors. By automatizing the segmentation of the electron microscopy images we guarantee an unbiased approach and we will be able to verify the connectome on several subjects. For the third project we moved from image processing applications to biological modeling. Because of the high complexity of even small biological systems it is necessary to analyze them with the help of computational tools. The term in silico was coined to refer to such computational models of biological systems. We designed an in silico model of the TNF (Tumor necrosis factor) ligand and its two principal receptors. This biological system is of high relevance because it is involved in the inflammation process. Inflammation is of most importance as protection mechanism but it can also lead to complicated diseases (e.g. cancer). Chronic inflammation processes can be particularly dangerous in the brain. In order to better understand the dynamics that govern the TNF system we created a model using the BioNetGen language. This is a rule based language that allows one to simulate systems where multiple agents are governed by a single rule. Using our model we characterized the TNF system and hypothesized about the relation of the ligand with each of the two receptors. Our hypotheses can be later used to define drug targets in the system or possible treatments for chronic inflammation or lack of the inflammatory response. The final project deals with the protein folding problem. In our organism proteins are folded all the time, because only in their folded conformation are proteins capable of doing their job (with some very few exceptions). This folding process presents a great challenge for science because it has been shown to be an NP problem. NP means non deterministic Polynomial time problem. This basically means that this kind of problems cannot be efficiently solved. Nevertheless, somehow the body is capable of folding a protein in just milliseconds. This phenomenon puzzles not only biologists but also mathematicians. In mathematics NP problems have been studied for a long time and it is known that given the solution to one NP problem we could solve many of them (i.e. NP-complete problems). If we manage to understand how nature solves the protein folding problem then we might be able to apply this solution to many other problems. Our research intends to contribute to this discussion. Unfortunately, not to explain how nature solves the protein folding problem, but to explain that it does not solve the problem at all. This seems contradictory since I just mentioned that the body folds proteins all the time, but our hypothesis is that the organisms have learned to solve a simplified version of the NP problem. Nature does not solve the protein folding problem in its full complexity. It simply solves a small instance of the problem. An instance which is as simple as a convex optimization problem. We formulate the protein folding problem as an optimization problem to illustrate our claim and present some toy examples to illustrate the formulation. If our hypothesis is true, it means that protein folding is a simple problem. So we just need to understand and model the conditions of the vicinity inside the cell at the moment the folding process occurs. Once we understand this starting conformation and its influence in the folding process we will be able to design treatments for amyloid diseases such as Alzheimer's and Parkinson's. In summary this thesis project contributes to the neurobiology research field from four different fronts. Two are practical contributions with immediate benefits, such as the calcium imaging video analysis tool and the TNF in silico model. The neuron nuclei segmentation is a contribution for the near future. A step towards the full annotation of the c.elegans connectome and later for the reconstruction of the connectome of other species. And finally, the protein folding project is a first impulse to change the way we conceive the protein folding process in nature. We try to point future research in a novel direction, where the amino code is not the most relevant characteristic of the process but the conditions within the cell.
... There are important potential advantages that seem to be relevant for the particular simulation of consciousness and its disorders (Markram, 2013): ...
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Modeling and simulations have gained a leading position in contemporary attempts to describe, explain, and quantitatively predict the human brain’s operations. Computer models are highly sophisticated tools developed to achieve an integrated knowledge of the brain with the aim of overcoming the actual fragmentation resulting from different neuroscientific approaches. In this paper we investigate the plausibility of simulation technologies for emulation of consciousness and the potential clinical impact of large-scale brain simulation on the assessment and care of disorders of consciousness (DOCs), e.g., Coma, Vegetative State/Unresponsive Wakefulness Syndrome, Minimally Conscious State. Notwithstanding their technical limitations, we suggest that simulation technologies may offer new solutions to old practical problems, particularly in clinical contexts. We take DOCs as an illustrative case, arguing that the simulation of neural correlates of consciousness is potentially useful for improving treatments of patients with DOCs.
... Achievements by virtue of this technology portfolio are foreseen to unleash novel insights on how brain reacts against emotions, gets stimulated when undertaking tasks of different nature, or degrades under distinct mental illnesses. Therefore, huge research efforts are being currently invested by worldwide funding agencies to support novel approaches, methods and techniques aimed at acquiring a deeper knowledge and confronting the challenges in this vibrant field [15,11]. ...
Chapter
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In the last few years the research community has striven to achieve a thorough understanding of the brain activity when the subject under analysis undertakes both mechanical tasks and purely mental exercises. One of the most avant-garde approaches in this regard is the discovery of connectivity patterns among different parts of the human brain unveiled by very diverse sources of information (e.g. magneto- or electro-encephalography – M/EEG, functional and structural Magnetic Resonance Imaging – fMRI and sMRI, or positron emission tomography – PET), coining the so-called brain connectomics discipline. Surprisingly, even though contributions related to the brain connectome abound in the literature, far too little attention has been paid to the exploitation of such complex spatial-temporal patterns to classify the task performed by the subject while brain signals are being registered. This manuscript covers this research niche by elaborating on the extraction of topological features from the graph modeling the brain connectivity under different tasks. By resorting to public information from the Human Connectome Project, the work will show that a selected subset of topological predictors from M/EEG connectomes suffices for accurately predicting (with average accuracy scores of up to 95%) the task performed by the subject at hand, further insights given on their predictive power when the M/EEG connectivity is inferred over different frequency bands.
... For instance, the flagship project (HBP) transformed its original drive (for a better understanding of brain) into a "viewing neuroscope" IT platform built largely on preexisting data. Progress is expected mostly from an alliance of deep learning, neuroinformatics, and neuromorphic computation, and promised to be quantitative enough to sustain virtual medicine applications (135). ...
Article
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New technologies in neuroscience generate reams of data at an exponentially increasing rate, spurring the design of very-large-scale data-mining initiatives. Several supranational ventures are contemplating the possibility of achieving, within the next decade(s), full simulation of the human brain.
Article
The first part of the article discusses the book The Myth of Artificial Intelligence by American scientist and entrepre-neur E. Larson, which focuses on debunking some myths about artificial intelligence. These myths, which have persist-ed for over half a century, suggest that the emergence of human-like ("general") AI and eventually superintelligence is inevitable, occurring naturally as AI systems evolve. The book criticizes these myths in two ways: scientific and social. It is shown that machine learning does not lead to general AI, and the myth of AI makes human potential look weaker. The second part of the article considers the problem of understanding. The concept of cognitive semantics is proposed, based on the ideas of J. Lakoff, S. Pinker, A. Damasio and A. Seth. In particular, it is noted that: understanding is an interpretation in terms of a person’s picture of the world; the picture of the world is constructed by our brain, and it is structured through the categorization of human experience; meanings (senses) are formed earlier than conceptual structures are formed; biological goals underlie meanings; not only the brain but also the body participates in cognitive processes, and understanding is associated with actions in the environment, knowledge of which is contained in the picture of the world. The article concludes by pointing out dead ends, difficulties and dangers on the path to general AI.
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Roboticists and neuroscientists are interested in understanding and reproducing the neural and cognitive mechanisms behind the human ability to interact with unknown and changing environments as well as to learn and execute fine movements. In this paper, we review the system-level neurocomputational models of the human motor system, and we focus on biomimetic models simulating the functional activity of the cerebellum, the basal ganglia, the motor cortex, and the spinal cord, which are the main central nervous system areas involved in the learning, execution, and control of movements. We review the models that have been proposed from the early of 1970s, when the first cerebellar model was realized, up to nowadays, when the embodiment of these models into robots acting in the real world and into software agents acting in a virtual environment has become of paramount importance to close the perception-cognition-action cycle. This review shows that neurocomputational models have contributed to the comprehension and reproduction of neural mechanisms underlying reaching movements, but much remains to be done because a whole model of the central nervous system controlling musculoskeletal robots is still missing.
Chapter
Mind uploading is the futurist idea of emulating all brain processes of an individual on a computer. Progress towards achieving this technology is currently limited by society's capability to study the human brain and the development of complex artificial neural networks capable of emulating the brain's architecture. The goal of this chapter is to provide a brief history of both categories, discuss the progress made, and note the roadblocks hindering future research. Then, by examining the roadblocks of neuroscience and artificial intelligence together, this chapter will outline a way to overcome their respective limitations by using the other field's strengths.
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Rare diseases are estimated to affect more than one in ten Americans. However, most patients with a rare disease face significant emotional, physical, and social challenges. To better understand the burden of disease and unmet needs, the US Food and Drug Administration (FDA) conducts and supports multiple patient engagement platforms. We analyzed summaries from these discussions to identify commonalities among patients with disparate rare diseases, the results of which could inform priorities for cross-disease policies and medical product development. We conducted a qualitative analysis of patient engagement session summaries to investigate shared experiences across rare diseases. Cross-disease similarities were identified within four dimensions: product development/regulatory, clinical/physical, social/psychological, and economic/financial. Summaries from 29 rare diseases were included in our analyses. Within the product development/regulatory dimension, we observed that patients and caregivers across rare diseases shared the desire for development of medical products that cured their disease or improved their overall quality of life. In the clinical/physical dimension, we found that patients had numerous common symptoms, including pain and fatigue. In the social/psychological dimension, we observed significant negative impact on mental health. Within the economic/financial dimension, patients and caregivers shared that disease burden caused significant financial hardships. We found remarkable similarities among patients with rare diseases across all four dimensions. Our results indicate that, even among rare diseases with diverse etiologies, patients share numerous commonalties due to their diseases: a lack of effective treatment options, certain physical symptoms, mental health challenges, and financial concerns.
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This paper demonstrates the use of augmented reality (AR) to teach the fundamental aspects of the human brain and guide proper EEG electrode placement. The proposed application consists of two main parts: (1) the proposed marker-based AR system uses the Vuforia technique to determine the dimension of the head to create the virtual brain and virtual EEG electrodes; and (2) user interaction and implementation. We performed two experiments using a phantom head to verify the size and workspace area of the marker and validated the position of the virtual electrode with ground truth data. The results showed that the proposed method can be employed for electrode placement guidance in the recommended range. We aim to use the proposed system for beginners. We will further test the system with human heads to evaluate the usability and determine key areas for application improvement.
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Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this Roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.
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Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In this architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex and unstructured data as our brain does. Neuromorphic computing systems are aimed at addressing these needs. The human brain performs about 10^15 calculations per second using 20W and a 1.2L volume. By taking inspiration from biology, new generation computers could have much lower power consumption than conventional processors, could exploit integrated non-volatile memory and logic, and could be explicitly designed to support dynamic learning in the context of complex and unstructured data. Among their potential future applications, business, health care, social security, disease and viruses spreading control might be the most impactful at societal level. This roadmap envisages the potential applications of neuromorphic materials in cutting edge technologies and focuses on the design and fabrication of artificial neural systems. The contents of this roadmap will highlight the interdisciplinary nature of this activity which takes inspiration from biology, physics, mathematics, computer science and engineering. This will provide a roadmap to explore and consolidate new technology behind both present and future applications in many technologically relevant areas.
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Evidence-based medicine (EBM) and Evidence-based practice (EBP) are sets of standards and procedures created to search, verify, and select up-to-date findings implemented by medical staff as a basis for decision-making process in a daily clinical practice. Despite efforts of scientists and clinicians, neurorehabiltiation is regarded as a difficult area for EBM/EBP practices due to huge diversity of cases, clinical pictures, interventions, and scientific methodologies. More advanced tasks, including application of brain-computer interfaces and neuroprosteheses, show the need for a new approach from medical practitioners. This chapter presents challenges, barriers, and solutions in the aforementioned area based on the personal experiences of the authors. Visualisation tools provide cognitive support for social context, cooperation patterns, and data interpretation. Taking into consideration that social issues may extend the visibility of the results and allow for easier dissemination of the results, the aim was to show how visualisation helps identify cooperation networks and disseminate research results.
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Background: The spectrum of disorders of the brain is large, covering hundreds of disorders that are listed in either the mental or neurological disorder chapters of the established international diagnostic classification systems. These disorders have a high prevalence as well as short- and long-term impairments and disabilities. Therefore they are an emotional, financial and social burden to the patients, their families and their social network. In a 2005 landmark study, we estimated for the first time the annual cost of 12 major groups of disorders of the brain in Europe and gave a conservative estimate of €386 billion for the year 2004. This estimate was limited in scope and conservative due to the lack of sufficiently comprehensive epidemiological and/or economic data on several important diagnostic groups. We are now in a position to substantially improve and revise the 2004 estimates. In the present report we cover 19 major groups of disorders, 7 more than previously, of an increased range of age groups and more cost items. We therefore present much improved cost estimates. Our revised estimates also now include the new EU member states, and hence a population of 514 million people. Aims: To estimate the number of persons with defined disorders of the brain in Europe in 2010, the total cost per person related to each disease in terms of direct and indirect costs, and an estimate of the total cost per disorder and country. Methods: The best available estimates of the prevalence and cost per person for 19 groups of disorders of the brain (covering well over 100 specific disorders) were identified via a systematic review of the published literature. Together with the twelve disorders included in 2004, the following range of mental and neurologic groups of disorders is covered: addictive disorders, affective disorders, anxiety disorders, brain tumor, childhood and adolescent disorders (developmental disorders), dementia, eating disorders, epilepsy, mental retardation, migraine, multiple sclerosis, neuromuscular disorders, Parkinson's disease, personality disorders, psychotic disorders, sleep disorders, somatoform disorders, stroke, and traumatic brain injury. Epidemiologic panels were charged to complete the literature review for each disorder in order to estimate the 12-month prevalence, and health economic panels were charged to estimate best cost-estimates. A cost model was developed to combine the epidemiologic and economic data and estimate the total cost of each disorder in each of 30 European countries (EU27+Iceland, Norway and Switzerland). The cost model was populated with national statistics from Eurostat to adjust all costs to 2010 values, converting all local currencies to Euro, imputing costs for countries where no data were available, and aggregating country estimates to purchasing power parity adjusted estimates for the total cost of disorders of the brain in Europe 2010. Results: The total cost of disorders of the brain was estimated at €798 billion in 2010. Direct costs constitute the majority of costs (37% direct healthcare costs and 23% direct non-medical costs) whereas the remaining 40% were indirect costs associated with patients' production losses. On average, the estimated cost per person with a disorder of the brain in Europe ranged between €285 for headache and €30,000 for neuromuscular disorders. The European per capita cost of disorders of the brain was €1550 on average but varied by country. The cost (in billion €PPP 2010) of the disorders of the brain included in this study was as follows: addiction: €65.7; anxiety disorders: €74.4; brain tumor: €5.2; child/adolescent disorders: €21.3; dementia: €105.2; eating disorders: €0.8; epilepsy: €13.8; headache: €43.5; mental retardation: €43.3; mood disorders: €113.4; multiple sclerosis: €14.6; neuromuscular disorders: €7.7; Parkinson's disease: €13.9; personality disorders: €27.3; psychotic disorders: €93.9; sleep disorders: €35.4; somatoform disorder: €21.2; stroke: €64.1; traumatic brain injury: €33.0. It should be noted that the revised estimate of those disorders included in the previous 2004 report constituted €477 billion, by and large confirming our previous study results after considering the inflation and population increase since 2004. Further, our results were consistent with administrative data on the health care expenditure in Europe, and comparable to previous studies on the cost of specific disorders in Europe. Our estimates were lower than comparable estimates from the US. Discussion: This study was based on the best currently available data in Europe and our model enabled extrapolation to countries where no data could be found. Still, the scarcity of data is an important source of uncertainty in our estimates and may imply over- or underestimations in some disorders and countries. Even though this review included many disorders, diagnoses, age groups and cost items that were omitted in 2004, there are still remaining disorders that could not be included due to limitations in the available data. We therefore consider our estimate of the total cost of the disorders of the brain in Europe to be conservative. In terms of the health economic burden outlined in this report, disorders of the brain likely constitute the number one economic challenge for European health care, now and in the future. Data presented in this report should be considered by all stakeholder groups, including policy makers, industry and patient advocacy groups, to reconsider the current science, research and public health agenda and define a coordinated plan of action of various levels to address the associated challenges. Recommendations: Political action is required in light of the present high cost of disorders of the brain. Funding of brain research must be increased; care for patients with brain disorders as well as teaching at medical schools and other health related educations must be quantitatively and qualitatively improved, including psychological treatments. The current move of the pharmaceutical industry away from brain related indications must be halted and reversed. Continued research into the cost of the many disorders not included in the present study is warranted. It is essential that not only the EU but also the national governments forcefully support these initiatives.
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