Main description:

Written from the physicist's perspective, this book introduces computational neuroscience with in-depth contributions by system neuroscientists. The authors set forth a conceptual model for complex networks of neurons that incorporates important features of the brain. The computational implementation on supercomputers, discussed in detail, enables you to adapt the algorithm for your own research. Worked-out examples of applications are provided.

Contents:

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