Author response for "A computational neuroscience framework for quantifying warning signals"
Olivier PenacchioChristina G. HalpinInnes C. CuthillP. George LovellMatthew WheelwrightJohn SkelhornCandy RoweJulie M. Harris
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Computational neuroscience
What we call computational neuroscience involves construction of mathematical and numerical models for understanding cognitive phenomena. This issue is devoted to showing how it can also be used to help in the analysis of cognitive defects. Although the models may seem abstract to clinicians, they are based on the reality of brain anatomy. The theoretical papers presented here are connectionist : They posit a network of cells connected by synapses whose weights are modified during learning . Architecture of connectionist models has progressed and ramified considerably since they were first introduced, and we include some examples of the current state of the art. The final work presented here is concerned with the connection of the constructed models with clinical experience and experiment.
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Computational neuroscience
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Philosophy of language
Neurophilosophy
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This paper focuses on three connected to each other fundamental beliefs that appear to be unconsciously taken for granted at the basis of modern cognitive neuroscience: (i) the attractor hypothesis, (ii) the neural code hypothesis, and (iii) the supervenience hypothesis about human subjective experience. Their precise understanding and experimental verification is an important challenge that calls for a scientific paradigm change and needs to be addressed for future progress in cognitive neuroscience. The work presents a theoretical analysis and preliminary experimental and modeling results that suggest a roadmap to solving the challenge.
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Progress in understanding cognition requires a quantitative, theoretical framework, grounded in the other natural sciences and able to bridge between implementational, algorithmic and computational levels of explanation. I review recent results in neuroscience and cognitive biology that, when combined, provide key components of such an improved conceptual framework for contemporary cognitive science. Starting at the neuronal level, I first discuss the contemporary realization that single neurons are powerful tree-shaped computers, which implies a reorientation of computational models of learning and plasticity to a lower, cellular, level. I then turn to predictive systems theory (predictive coding and prediction-based learning) which provides a powerful formal framework for understanding brain function at a more global level. Although most formal models concerning predictive coding are framed in associationist terms, I argue that modern data necessitate a reinterpretation of such models in cognitive terms: as model-based predictive systems. Finally, I review the role of the theory of computation and formal language theory in the recent explosion of comparative biological research attempting to isolate and explore how different species differ in their cognitive capacities. Experiments to date strongly suggest that there is an important difference between humans and most other species, best characterized cognitively as a propensity by our species to infer tree structures from sequential data. Computationally, this capacity entails generative capacities above the regular (finite-state) level; implementationally, it requires some neural equivalent of a push-down stack. I dub this unusual human propensity "dendrophilia", and make a number of concrete suggestions about how such a system may be implemented in the human brain, about how and why it evolved, and what this implies for models of language acquisition. I conclude that, although much remains to be done, a neurally-grounded framework for theoretical cognitive science is within reach that can move beyond polarized debates and provide a more adequate theoretical future for cognitive biology.
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Білім берy қоғaмның экономикaлық дaмyының негізі, әлеyметтік тұрaқтылықтың фaкторлaрының бірі, хaлықтың рyхaни-aдaмгершілік әлеyетінің және интеллектyaлдық өсyінің қaйнaр көзі ретінде бaрлық yaқыттaрдa тaптырмaс құндылық болып есептеліп келеді. Aл қaзіргідей aдaм кaпитaлын қaлыптaстырy мен дaмытy мәселесін шешy негізгі міндет ретінде қaрaстырылaтын зaмaндa хaлықтың білімдік қaжеттіліктері өсіп, жоғaры, ортa aрнayлы, кәсіби қосымшa білім aлyғa үміткерлер сaны aртa түсyде. Бұғaн жayaп ретінде білім берy ұйымдaрының сaлaлaнyы aртып, әртүрлі типтегі оқy орындaрының сaны aртyдa, білім берyдің инфрaқұрылымы, бaсқaрy формaлaры, әдістемелік, ғылыми қызмет түрлері дaмyдa. Олaрды білім aлyшылaрдың жеке сұрaныстaры мен мүмкіндіктеріне бaғыттay күшейтілyде. Осығaн орaй білімнің сaпaсынa қойылaтын тaлaптaр aртып, бұл сaлaның әлеyметпен өзaрa әрекеттестігіне негізделген құрылымдық – қызметтік дaмyының көкейтестілігі aртyдa. Мaқaлaдa «серіктестік», «әлеyметтік серіктестік», «білімдегі әлеyметтік серіктестік» ұғым- дaрының мәні aшылып, олaрдың қaлыптaсy және дaмy үрдісіне шолy жaсaлaды, жоғaры оқy орындaрындa педaгогтaрды дaярлayдa әлеyметтік серіктестердің әлеyетін пaйдaлaнyдa бaсшылыққa aлынaтын ұстaнымдaр мен тиімді жолдaры сипaттaлaды. Түйін сөздер: серіктестік, әлеyметтік серіктестік, білімдегі әлеyметтік серіктестік, бірлескен әрекет ұстaнымдaры, әлеуметтік серіктестік әлеуеті. Обрaзовaние является основой экономического рaзвития обществa, одним из фaкторов социaль- ной стaбильности, источником дyховно-нрaвственного потенциaлa и интеллектyaльного ростa людей и во все временa считaлось незaменимой ценностью. И в нaстоящее время, когдa решение проблемы формировaния и рaзвития человеческого кaпитaлa рaссмaтривaется кaк основнaя зaдaчa, рaстyт обрaзовaтельные потребности людей, yвеличивaется количество желaющих полyчить высшее, среднее, специaльное, профессионaльное дополнительное обрaзовaние. В ответ нa это yсиливaется рaзветвленность обрaзовaтельных оргaнизaций, yвеличивaется количество обрaзовaтельных оргaни- зaций рaзличного типa, рaзвивaются инфрaстрyктyрa обрaзовaния, формы yпрaвления, методическaя и нayчнaя деятельность. Yсиливaется их ориентaция нa индивидyaльные потребности и возможности обyчaющихся. В связи с этим повышaются требовaния к кaчествy обрaзовaния, возрaстaет знaчение стрyктyрно-фyнкционaльного рaзвития этой сферы нa основе взaимодействия с обществом. В стaтье рaскрывaется знaчение понятий «пaртнерство», «социaльное пaртнерство», «социaльное пaртнерство в обрaзовaнии», рaссмaтривaется процесс их стaновления и рaзвития, описывaются рyко- водящие принципы и эффективные способы использовaния потенциaлa социaльных пaртнеров в подготовке педaгогических кaдров в высших yчебных зaведениях. Ключевые словa: партнерство, социaльное пaртнерство, социaльное пaртнерство в обрaзовaнии, принципы совместного действия, поненциал социального партнерство. Education is the basis of the economic development of society, one of the factors of social stability, a source of spiritual and moral potential and intellectual growth of people and has always been considered an irreplaceable value. And at the present time, when the solution of the problem of the formation and development of human capital is considered as the main task, the educational needs of people are growing, the number of people wishing to receive higher, secondary, special, professional additional education is increasing. In response to this, the branching of educational organizations is increasing, the number of educational organizations of various types is increasing, the infrastructure of education, forms of management, methodological and scientific activities are developing. Their focus on the individual needs and capabilities of students is increasing. In this regard, the requirements for the quality of education are increasing, the importance of the structural and functional development of this sphere on the basis of interaction with society is increasing. The article reveals the meaning of the concepts of "partnership", "social partnership", "social partnership in education", examines the process of their formation and development, describes the guidelines and effective ways to use the potential of social partners in the training of teachers in higher educational institutions. Keywords: partnership, social partnership, social partnership in education, principles of joint action, the potential of social partnership.
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Understanding complex neurobiological systems is one of the most difficult challenges in modern science. This book is focused on computational neuroscience, which provides a mathematical foundation and a rich set of computational approaches for understanding the principles and dynamics of the nervous system. Taking a bottom up strategy, the book offers comprehensive, step-by-step coverage on how to model the neuron and neural circuitry to understand the nervous system at multiple levels, from ion channels to networks. It has arisen from graduate courses to students in physical, mathematical and computer sciences, and can serve as an excellent text for courses in computational neuroscience.
Computational neuroscience
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Systems neuroscience
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Neurophysiology
Computational neuroscience
Biological neural network
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Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism (the notion that neural functions are shaped by these interactions) is core to some developmental theories. However, due to their complexity, understanding underlying developmental mechanisms is challenging. Elsewhere in neurobiology, a computational revolution has shown that mathematical models of hidden biological mechanisms can bridge observations with theory building. Can we build a similar computational framework yielding mechanistic insights for brain development? Here, we outline the conceptual and technical challenges of addressing this theory gap, and demonstrate that there is great potential in specifying brain development as mathematically defined processes operating within physical constraints. We provide examples, alongside broader ingredients needed, as the field explores computational explanations of system-wide development.
Computational neuroscience
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