Lectures in Supercomputational Neurosciences: Dynamics in Complex Brain Networks

by Graben, Peter beim

• ISBN13: 9783540731580
• ISBN10: 354073158X
• Edition: 1st
• Format: Hardcover
• Copyright: 2007-12-01
• Publisher: SPRINGER

Summary

Computational Neurosciences is a burgeoning field of research where only the combined effort of neuroscientists, biologists, psychologists, physicists, mathematicians, computer scientists, engineers and other specialists, e.g. from linguistics and medicine, seem to be able to expand the limits of our knowledge. The present volume is an introduction, largely from the physicists' perspective, to the subject matter with in-depth contributions by system neuroscientists. A conceptual model for complex networks of neurons is introduced that incorporates many important features of the real brain, such as various types of neurons, various brain areas, inhibitory and excitatory coupling and the plasticity of the network. The computational implementation on supercomputers, which is introduced and discussed in detail in this book, will enable the readers to modify and adapt the algortihm for their own research. Worked-out examples of applications are presented for networks of Morris-Lecar neurons to model the cortical connections of a cat's brain, supported with data from experimental studies. This book is particularly suited for graduate students and nonspecialists from related fields with a general science background, looking for a substantial but 'œhands-on' introduction to the subject matter.

Table of Contents

Neurophysiology
Foundations of Neurophysics	p. 3
Synapses and Neurons: Basic Properties and Their Use in Recognizing Environmental Signals	p. 49
Complex Networks
Structural Characterization of Networks Using the Cat Cortex as an Example	p. 77
Organization and Function of Complex Cortical Networks	p. 107
Synchronization Dynamics in Complex Networks	p. 135
Synchronization Analysis of Neuronal Networks by Means of Recurrence Plots	p. 177
Cognition and Higher Perception
Neural and Cognitive Modeling with Networks of Leaky Integrator Units	p. 195
A Dynamic Model of the Macrocolumn	p. 225
Implementations
Building a Large-Scale Computational Model of a Cortical Neuronal Network	p. 251
Maintaining Causality in Discrete Time Neuronal Network Simulations	p. 267
Sequential and Parallel Implementation of Networks	p. 279
Applications
Parametric Studies on Networks of Morris-Lecar Neurons	p. 319
Traversing Scales: Large Scale Simulation of the Cat Cortex Using Single Neuron Models	p. 331
Parallel Computation of Large Neuronal Networks with Structured Connectivity	p. 343
Index	p. 369
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