Computational neuroscience is a relatively new but rapidly expanding area of research which is becoming increasingly influential in shaping the way scientists think about the brain. Computational approaches have been applied at all levels of analysis, from detailed models of single-channel function, transmembrane currents, single-cell electrical activity, and neural signaling to broad theories of sensory perception, memory, and cognition. This book provides a snapshot of this exciting new field by bringing together chapters on a diversity of topics from some of its most important contributors. This includes chapters on neural coding in single cells, in small networks, and across the entire cerebral cortex, visual processing from the retina to object recognition, neural processing of auditory, vestibular, and electromagnetic stimuli, pattern generation, voluntary movement and posture, motor learning, decision-making and cognition, and algorithms for pattern recognition. Each chapter provides a bridge between a body of data on neural function and a mathematical approach used to interpret and explain that data. These contributions demonstrate how computational approaches have become an essential tool which is integral in many aspects of brain science, from the interpretation of data to the design of new experiments, and to the growth of our understanding of neural function.

Key Features

Includes contributions by some of the most influential people in the field of computational neuroscience Demonstrates how computational approaches are being used today to interpret experimental data Covers a wide range of topics from single neurons, to neural systems, to abstract models of learning


Neuroscientists, psychologists, mathematicians, and computer scientists

Table of Contents

The neuronal transfer function: contributions from voltage and time-dependent mechanisms E.P. Cook, A.C. Wilhelm, J.A. Guest, Y. Liang, N.Y. Masse and C.M. Colbert (Montreal QC, Canada and Houston, TX, USA). A simple growth model constructs critical avalanche networks L.F. Abbott and R. Rohrkemper (New York, NY, USA and Zurich, Switzerland). The dynamics of visual responses in the primary visual cortex R. Shapley, M. Hawken and D. Xing (New York, NY, USA). A quantitative theory of immediate visual recognition T. Serre, G. Kreiman, M. Kouh, C. Cadieu, U. Knoblich and T. Poggio (Boston, MA, USA). Attention in hierarchical models of object recognition D.B. Walther and C. Koch (Urbana, IL, and Pasadena,CA, USA). Towards a unified theory of neocortex: laminar cortical circuits for vision and cognition S. Grossberg (Boston, MA, USA). Real-time neural coding of memory J.Z. Tsien (Boston, MA, USA). Beyond timing in the auditory brainstem: intensity in the avian cochlear nucleus angularis K.M. MacLeod and C.E. Carr (College Park, MD, USA). Neural strategies for optimal processing of sensory signals L. Maler (Ottawa, ON, Canada). Coordinate transformations and sensory integration in the detection of spatial orientation and self-motion: from models to experiments A.M. Green and D.E. Angelaki (Montreal, QC, Canada and St. Louis, MO, USA). Sensorimotor optimization in higher dimensions . Tweed (Toronto, ON,USA). How tightly tuned are network parameters? Insight from computational and experimental studies in small rhythmic motor networks E. Marder, A.-E. Tobin and R. Grashow (Waltham, MA, USA). Spatial organization and state-dependent mechanisms for respiratory rhythm and


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© 2007
Elsevier Science
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About the editors

Paul Cisek

Affiliations and Expertise

University of Montreal, Quebec, Canada

Trevor Drew

Affiliations and Expertise

University of Montreal, Quebec, Canada

John Kalaska

Affiliations and Expertise

University of Montreal, Quebec, Canada