Brain dynamics : synchronization and activity patterns in pulse-coupled neural nets with delays and noise
Author(s)
Bibliographic Information
Brain dynamics : synchronization and activity patterns in pulse-coupled neural nets with delays and noise
（Springer series in synergetics）
Springer, c2002
Available at 44 libraries
Note
With 82 figures
Includes bibliographical references (p. [229]-240) and index
Description and Table of Contents
Description
This book addresses a large variety of models in mathematical and computational neuroscience. It is written for the experts as well as for graduate students wishing to enter this fascinating field of research. The author studies the behaviour of large neural networks composed of many neurons coupled by spike trains. He devotes the main part to the synchronization problem. He presents neural net models more realistic than the conventional ones by taking into account the detailed dynamics of axons, synapses and dendrites, allowing rather arbitrary couplings between neurons. He gives a complete stability analysis that goes significantly beyond what has been known so far. He also derives pulse-averaged equations including those of the Wilson--Cowan and the Jirsa--Haken--Nunez types and discusses the formation of spatio-temporal neuronal activity patterns. An analysis of phase locking via sinusoidal couplings leading to various kinds of movement coordination is included.
Table of Contents
From the Contents: Basic Experimental Facts and Theoretical Tools.- Introduction.- The Neuron-Building Block of the Brain.- Neuronal Cooperativity.- Spikes, Phases, Noise: How to Describe them Mathematically? We Learn a Few Tricks and Some Important Concepts.- Spiking in Neural Nets.- The Lighthouse Model. Two Coupled Neurons.- The Lighthouse Model. Many Coupled Neurons.- Integrate and Fire Models (IFM).- Many Neurons, General Case, Connection with Integrate and Fire Model.- Phase Locking, Coordination and Spatio-Temporal Patterns.- Phase Locking via Sinusoidal Couplings.- Pulse-Averaged Equations.- Conclusion.- The Single Neuron.- Conclusion and Outlook.- References.
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