Computational neurosciences and systems biology are among the main domains of life science research where mathematical modeling made a difference. This book introduces the many different types of computational studies one can develop to study neuronal systems. It is aimed at undergraduate students starting their research in computational neurobiology or more senior researchers who would like, or need, to move towards computational approaches. Based on their specific project, the readers would then move to one of the more specialized excellent textbooks available in the field. The first part of the book deals with molecular systems biology. Functional genomics is introduced through examples of transcriptomics and proteomics studies of neurobiological interest. Quantitative modelling of biochemical systems is presented in homogeneous compartments and using spatial descriptions. A second part deals with the various approaches to model single neuron physiology, and naturally moves to neuronal networks. A division is focused on the development of neurons and neuronal systems and the book closes on a series of methodological chapters. From the molecules to the organ, thinking at the level of systems is transforming biology and its impact on society. This book will help the reader to hop on the train directly in the tank engine.

It is generally understood that the present approachs to computing do not have the performance, flexibility, and reliability of biological information processing systems. Although there is a comprehensive body of knowledge regarding how information processing occurs in the brain and central nervous system this has had little impact on mainstream computing so far. This book presents a broad spectrum of current research into biologically inspired computational systems and thus contributes towards developing new computational approaches based on neuroscience. The 39 revised full papers by leading researchers were carefully selected and reviewed for inclusion in this anthology. Besides an introductory overview by the volume editors, the book offers topical parts on modular organization and robustness, timing and synchronization, and learning and memory storage.

This volume includes papers originally presented at the 8th annual Computational Neuroscience meeting (CNS'99) held in July of 1999 in Pittsburgh, Pennsylvania. The CNS meetings bring together computational neuroscientists representing many different fields and backgrounds as well as experimental preparations and theoretical approaches. The papers published here range across vast levels of scale from cellular mechanisms to cognitive brain studies. The subjects of the research include many different preparations from invertebrates to humans. In all cases the work described in this volume is focused on understanding how nervous systems compute. The research described includes subjects like neural coding and neuronal dendrites and reflects a trend towards forging links between cognitive research and neurobiology. Accordingly, this volume reflects the breadth and depth of current research in computational neuroscience taking place throughout the world.

Explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition. In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience. It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology. Dynamical Systems in Neuroscience presents a systematic study of the relationship of electrophysiology, nonlinear dynamics, and computational properties of neurons. It emphasizes that information processing in the brain depends not only on the electrophysiological properties of neurons but also on their dynamical properties. The book introduces dynamical systems, starting with one- and two-dimensional Hodgkin-Huxley-type models and continuing to a description of bursting systems. Each chapter proceeds from the simple to the complex, and provides sample problems at the end. The book explains all necessary mathematical concepts using geometrical intuition; it includes many figures and few equations, making it especially suitable for non-mathematicians. Each concept is presented in terms of both neuroscience and mathematics, providing a link between the two disciplines. Nonlinear dynamical systems theory is at the core of computational neuroscience research, but it is not a standard part of the graduate neuroscience curriculum—or taught by math or physics department in a way that is suitable for students of biology. This book offers neuroscience students and researchers a comprehensive account of concepts and methods increasingly used in computational neuroscience. An additional chapter on synchronization, with more advanced material, can be found at the author's website, www.izhikevich.com.

Neuroscience Databases: A Practical Guide is the first book providing a comprehensive overview of these increasingly important databases. This volume makes the results of the Human Genome Project and other recent large-scale initiatives in the neurosciences available to a wider community. It extends the scope of bioinformatics from the molecular to the cellular, microcircuitry and systems levels, dealing for the first time with complex neuroscientific issues and leading the way to a new culture of data sharing and data mining necessary to successfully tackle neuroscience questions. Aimed at the novice user who wants to access the data, it provides clear and concise instructions on how to download the available data sets and how to use the software with a minimum of technical detail with most chapters written by the database creators themselves.

The evolution of human language has been discussed for centuries from different perspectives. Linguistic theory has proposed grammar as a core part of human language that has to be considered in this context. Recent advances in neurosciences have allowed us to take a new neurobiological look on the similarities and dissimilarities of cognitive capacities and their neural basis across both closely and distantly related species. A couple of decades ago the comparisons were mainly drawn between human and non-human primates, investigating the cytoarchitecture of particular brain areas and their structural connectivity. Moreover, comparative studies were conducted with respect to their ability to process grammars of different complexity. So far the available data suggest that non-human primates are able to learn simple probabilistic grammars, but not hierarchically structured complex grammars. The human brain, which easily learns both grammars, differs from the non-human brain (among others) in how two language-relevant brain regions (Broca’s area and superior temporal cortex) are connected structurally. Whether the more dominant dorsal pathway in humans compared to non-human primates is causally related to this behavioral difference is an issue of current debate. Ontogenetic findings suggest at least a correlation between the maturation of the dorsal pathway and the behavior to process syntactically complex structures, although a causal prove is still not available. Thus the neural basis of complex grammar processing in humans remains to be defined. More recently it has been reported that songbirds are also able to distinguish between sound sequences reflecting complex grammar. Interestingly, songbirds learn to sing by imitating adult song in a process not unlike language development in children. Moreover, the neural circuits supporting this behavior in songbirds bear anatomical and functional similarities to those in humans. In adult humans the fiber tract connecting the auditory cortex and motor cortex dorsally is known to be involved in the repetition of spoken language. This pathway is present already at birth and is taken to play a major role during language acquisition. In songbirds, detailed information exist concerning the interaction of auditory, motor and cortical-basal ganglia processing during song learning, and present a rich substrate for comparative studies. The scope of the Research Topic is to bring together contributions of researchers from different fields, who investigate grammar processing in humans, non-human primates and songbirds with the aim to find answers to the question of what constitutes the neurobiological basis of grammar learning. Open questions are: Which brain networks are relevant for grammar learning? Is there more than one dorsal pathway (one from temporal cortex to motor cortex and one to Broca’s area) and if so what are their functions? Has the ability to process sequences of a given hierarchical complexity evolved in different phylogenetic lines (birds, primates, other vocal production learners such as bats)? Is the presence of a sensory-to-motor circuit in humans a precondition for development of a dorsal pathway between the temporal cortex and Broca’s area? What role do subcortical structures (Basal Ganglia) play in vocal and grammar learning?

This book aims to illuminate theoretical and methodological advances in computational cultural neuroscience and the implications of these advances for philosophy. Philosophical studies in computational cultural neuroscience introduce core considerations such as culture and computation, and the role of scientific and technological progression for the advancement of cultural processes. The study of how cultural and biological factors shape human behaviour has been an important inquiry for centuries, and recent advances in the field of computational cultural neuroscience allow for novel insights into the computational foundations of cultural processes in the structural and functional organization of the nervous system. The author examines the computational foundations of the mind and brain across cultures and investigates the influence of culture on the computational mind and brain. The book explores recent advances in the field, providing novel insights on topics such as artificialism, reconstructionism, and intelligence. Philosophy of Computational Cultural Neuroscience is fascinating reading for students and academics in the field of neuroscience who wish to take a cultural or philosophical approach to their studies and research. This book is the winner of the International Cultural Neuroscience Society’s International Book Prize.

Progress in Molecular Biology and Translational Science provides a forum for discussion of new discoveries, approaches, and ideas in molecular biology. It contains contributions from leaders in their fields and abundant references. This volume brings together different aspects of, and approaches to, molecular and multi-scale modeling, with applications to a diverse range of neurological diseases. Mathematical and computational modeling offers a powerful approach for examining the interaction between molecular pathways and ionic channels in producing neuron electrical activity. It is well accepted that non-linear interactions among diverse ionic channels can produce unexpected neuron behavior and hinder a deep understanding of how ion channel mutations bring about abnormal behavior and disease. Interactions with the diverse signaling pathways activated by G protein coupled receptors or calcium influx adds an additional level of complexity. Modeling is an approach to integrate myriad data sources into a cohesive and quantitative model in order to evaluate hypotheses about neuron function. In particular, a validated model developed using in vitro data allows simulations of the response to in vivo like spatio-temporal patterns of synaptic input. Incorporating molecular signaling pathways into an electrical model, allows a greater range of models to be developed, ones that can predict the response to pharmaceuticals, many of which target neuromodulator pathways. Contributions from leading authorities Informs and updates on all the latest developments in the field

This book presents thoroughly revised tutorial papers based on lectures given by leading researchers at the 8th International Summer School on Neural Networks in Erice, Italy, in October/November 2003. The eight tutorial papers presented provide competent coverage of the field of cortical dynamics, consolidating recent theoretical and experimental results on the processing, transmission, and imprinting of information in the brain as well as on important functions of the cortical area, such as cortical rhythms, cortical neural plasticity, and their structural basis and functional significance. The book is divided in two topical sections on fundamentals of cortical dynamics and mathematical models of cortical dynamics.