Kinetic Models of Synaptic Transmission / Alain Destexhe, Zachary F. Mainen, Terrence J. Sejnowski / - Cable Theory for Dendritic Neurons / Wilfrid Rall, Hagai Agmon-Snir / - Compartmental Models of Complex Neurons / Idan Segev, Robert E. Burke / - Multiple Channels and Calcium Dynamics / Walter M. Yamada, Christof Koch, Paul R. Adams / - Modeling Active Dendritic Processes in Pyramidal Neurons / Zachary F. Mainen, Terrence J. Sejnowski / - Calcium Dynamics in Large Neuronal Models / Erik De Schutter, Paul Smolen / - Analysis of Neural Excitability and Oscillations / John Rinzel, Bard Ermentrout / - Design and Fabrication of Analog VLSI Neurons / Rodney Douglas, Misha Mahowald / - Principles of Spike Train Analysis / Fabrizio Gabbiani, Christof Koch / - Modeling Small Networks / Larry Abbott, Eve Marder / - Spatial and Temporal Processing in Central Auditory Networks / Shihab Shamma / - Simulating Large Networks of Neurons / Alexander D. Protopapas, Michael Vanier, James M. Bower / ...
Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists. The editors (in their introduction) and contributors explain the basic concepts behind modeling and avoid the use of high-level mathematics. The book is divided into four parts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters on attention, memory, and primate studies. Part II discusses neural network models of behavioral states such as alcohol dependence, learned helplessness, depression, and waking and sleeping. Part III presents neural network models of neuropsychological tests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the Stroop Test. Finally, part IV describes the application of neural network models to dementia: models of acetycholine and memory, verbal fluency, Parkinsons disease, and Alzheimer's disease. Contributors J. Wesson Ashford, Rajendra D. Badgaiyan, Jean P. Banquet, Yves Burnod, Nelson Butters, John Cardoso, Agnes S. Chan, Jean-Pierre Changeux, Kerry L. Coburn, Jonathan D. Cohen, Laurent Cohen, Jose L. Contreras-Vidal, Antonio R. Damasio, Hanna Damasio, Stanislas Dehaene, Martha J. Farah, Joaquin M. Fuster, Philippe Gaussier, Angelika Gissler, Dylan G. Harwood, Michael E. Hasselmo, J, Allan Hobson, Sam Leven, Daniel S. Levine, Debra L. Long, Roderick K. Mahurin, Raymond L. Ownby, Randolph W. Parks, Michael I. Posner, David P. Salmon, David Servan-Schreiber, Chantal E. Stern, Jeffrey P. Sutton, Lynette J. Tippett, Daniel Tranel, Bradley Wyble
The purpose of this book is to introduce and survey the various quantitative methods which have been proposed for describing, simulating, embodying, or characterizing the processing of electrical signals in nervous systems. We believe that electrical signal processing is a vital determinant of the functional organization of the brain, and that in unraveling the inherent complexities of this processing it will be essential to utilize the methods of quantification and modeling which have led to crowning successes in the physical and engineering sciences. In comprehensive terms, we conceive neural modeling to be the attempt to relate, in nervous systems, function to structure on the basis of operation. Sufficient knowledge and appropriate tools are at hand to maintain a serious and thorough study in the area. However, work in the area has yet to be satisfactorily integrated within contemporary brain research. Moreover, there exists a good deal of inefficiency within the area resulting from an overall lack of direction, critical self-evaluation, and cohesion. Such theoretical and modeling studies as have appeared exist largely as fragmented islands in the literature or as sparsely attended sessions at neuroscience conferences. In writing this book, we were guided by three main immediate objectives. Our first objective is to introduce the area to the upcoming generation of students of both the hard sciences and psychological and biological sciences in the hope that they might eventually help bring about the contributions it promises.
An overview of the techniques used in modern neuroscience research with the emphasis on showing how different techniques can optimally be combined in the study of problems that arise at some levels of nervous system organization. This is essentially a working tool for the scientist in the laboratory and clinic, providing detailed step-by-step protocols with tips and recommendations. Most chapters and protocols are organized such that they can be used independently, while cross-references between the chapters, a glossary, a list of suppliers and appendices provide further help.
Designed primarily as an introduction to realistic modeling methods, Computational Neuroscience: Realistic Modeling for Experimentalists focuses on methodological approaches, selecting appropriate methods, and identifying potential pitfalls. The author addresses varying levels of complexity, from molecular interactions within single neurons to the
A fascinating exploration of the human brain that combines “the leading edge of consciousness science with surprisingly personal and philosophical reflection . . . shedding light on how scientists really think”—this is “science writing at its best” (Times Higher Education). In which a scientist searches for an empirical explanation for phenomenal experience, spurred by his instinctual belief that life is meaningful. What links conscious experience of pain, joy, color, and smell to bioelectrical activity in the brain? How can anything physical give rise to nonphysical, subjective, conscious states? Christof Koch has devoted much of his career to bridging the seemingly unbridgeable gap between the physics of the brain and phenomenal experience. This engaging book—part scientific overview, part memoir, part futurist speculation—describes Koch’s search for an empirical explanation for consciousness. Koch recounts not only the birth of the modern science of consciousness but also the subterranean motivation for his quest—his instinctual (if “romantic”) belief that life is meaningful. Koch describes his own groundbreaking work with Francis Crick in the 1990s and 2000s and the gradual emergence of consciousness (once considered a “fringy” subject) as a legitimate topic for scientific investigation. Present at this paradigm shift were Koch and a handful of colleagues, including Ned Block, David Chalmers, Stanislas Dehaene, Giulio Tononi, Wolf Singer, and others. Aiding and abetting it were new techniques to listen in on the activity of individual nerve cells, clinical studies, and brain-imaging technologies that allowed safe and noninvasive study of the human brain in action. Koch gives us stories from the front lines of modern research into the neurobiology of consciousness as well as his own reflections on a variety of topics, including the distinction between attention and awareness, the unconscious, how neurons respond to Homer Simpson, the physics and biology of free will, dogs, Der Ring des Nibelungen, sentient machines, the loss of his belief in a personal God, and sadness. All of them are signposts in the pursuit of his life's work—to uncover the roots of consciousness.
Much research focuses on the question of how information is processed in nervous systems, from the level of individual ionic channels to large-scale neuronal networks, and from "simple" animals such as sea slugs and flies to cats and primates. New interdisciplinary methodologies combine a bottom-up experimental methodology with the more top-down-driven computational and modeling approach. This book serves as a handbook of computational methods and techniques for modeling the functional properties of single and groups of nerve cells. The contributors highlight several key trends: (1) the tightening link between analytical/numerical models and the associated experimental data, (2) the broadening of modeling methods, at both the subcellular level and the level of large neuronal networks that incorporate real biophysical properties of neurons as well as the statistical properties of spike trains, and (3) the organization of the data gained by physical emulation of the nervous system components through the use of very large scale circuit integration (VLSI) technology. The field of neuroscience has grown dramatically since the first edition of this book was published nine years ago. Half of the chapters of the second edition are completely new; the remaining ones have all been thoroughly revised. Many chapters provide an opportunity for interactive tutorials and simulation programs. They can be accessed via Christof Koch's Website. Contributors Larry F. Abbott, Paul R. Adams, Hagai Agmon-Snir, James M. Bower, Robert E. Burke, Erik de Schutter, Alain Destexhe, Rodney Douglas, Bard Ermentrout, Fabrizio Gabbiani, David Hansel, Michael Hines, Christof Koch, Misha Mahowald, Zachary F. Mainen, Eve Marder, Michael V. Mascagni, Alexander D. Protopapas, Wilfrid Rall, John Rinzel, Idan Segev, Terrence J. Sejnowski, Shihab Shamma, Arthur S. Sherman, Paul Smolen, Haim Sompolinsky, Michael Vanier, Walter M. Yamada
