Edited by
Paul Cisek, University of Montreal, Quebec, Canada
Trevor Drew, University of Montreal, Quebec, Canada
John Kalaska, University of Montreal, Quebec, Canada
Description
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.
Included in series
Progress in Brain Research
Audience:
Neuroscientists, psychologists, mathematicians, and computer scientists
