Lectures in Supercomputational Neuroscience

Computational Neuroscience 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 "hands-on" introduction to the subject matter.

Texte du rabat

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.

Contenu

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.