On the Phase-Space Dynamics of Systems of Spiking Neurons. I: Model and Experiments

Authors:
Arunava Banerjee
Affiliations:
Department of Computer Science, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, U.S.A.
Venue:
Neural Computation
Year:
2001

Citing 5
Cited 2

Multilayer feedforward networks are universal approximators

Neural Networks
On the computational power of neural nets

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Constructing deterministic finite-state automata in recurrent neural networks

Journal of the ACM (JACM)
The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System

The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System
Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey

Neural Computation

Dynamical constraints on using precise spike timing to compute in recurrent cortical networks

Neural Computation
On the maximization of information flow between spiking neurons

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

We investigate the phase-space dynamics of a general model of local systems of biological neurons in order to deduce the salient dynamical characteristics of such systems. In this article, we present a detailed exposition of an abstract dynamical system that models systems of biological neurons. The abstract system is based on a limited set of realistic assumptions and thus accommodates a wide range of neuronal models. Simulation results are presented for several instantiations of the abstract system, each modeling a typical neocortical column to a different degree of accuracy. The results demonstrate that the dynamics of the systems are generally consistent with that observed in neurophysiological experiments. They reveal that the qualitative behavior of the class of systems can be classified into three distinct categories: quiescence, intense periodic activity resembling a state of seizure, and sustained chaos over the range of intrinsic activity typically associated with normal operational conditions in the neocortex. We discuss basic ramifications of this result with regard to the computational nature of neocortical neuronal systems.