Nonlinear Dynamics in Computational Neuroscience

by Fernando Corinto (Editor), Alessandro Torcini (Editor)

Overview

This book provides an essential overview of computational neuroscience. It addresses a broad range of aspects, from physiology to nonlinear dynamical approaches to understanding neural computation, and from the simulation of brain circuits to the development of engineering devices and platforms for neuromorphic computation. Written by leading experts in such diverse fields as neuroscience, physics, psychology, neural engineering, cognitive science and applied mathematics, the book reflects the remarkable advances that have been made in the field of computational neuroscience, an emerging discipline devoted to the study of brain functions in terms of the information-processing properties of the structures forming the nervous system. The contents build on the workshop “Nonlinear Dynamics in Computational Neuroscience: from Physics and Biology to ICT,” which was held in Torino, Italy in September 2015.

Product Details

ISBN-13:	9783319710488
Publisher:	Springer-Verlag New York, LLC
Publication date:	06/19/2018
Series:	PoliTO Springer Series
Sold by:	Barnes & Noble
Format:	eBook
File size:	7 MB

About the Author

Fernando Corinto is professor of Circuit Theory at Politecnico di Torino, Italy. His research activity focuses mainly on nonlinear circuits and systems, locally coupled nonlinear/nanoscale networks and memristor nanotechnology.
Alessandro Torcini is professor of Theoretical Physics at University of Cergy-Pontoise, France. His research activity focuses on the dynamics of complex and bio-inspired systems, in particular computational neuroscience. 

Table of Contents

Introduction.- Next generation neural mass models.- Unraveling brain modularity through its slow oscillations.- Characterization of Neural Signals in Preclinical Studies of Neural Plasticity using Nonlinear Time Series Analysis.- Functional Cliques in Developmentally Correlated Neural Networks.- Chimera states in pulse coupled neural networks: the influence of dilution and noise.- Nanotechnologies for Neurosciences.- Memristor and Memristor Circuit Modelling based on Methods of Nonlinear System Theory.- A memristor-based cell for complexity.