Comparison of single neuron models in terms of synchronization propensity

Abstract

A plausible model for coherent perception is the synchronization of chaotically distributed neural spike trains over wide cortical areas. A recently introduced propensity criterion provides a tool for a quantitative comparison of different neuron models in terms of their ability to synchronize to an applied perturbation. We explore the propensity of several systems and indicate the requirements to be satisfied by a plausible candidate for modeling neuronal activity. Our results show that the conflicting requirements of stability and sensitivity leading to high propensity to synchronization can be satisfied by a strongly nonuniform attractor made of two distinct regions: a saddle focus plus a sufficiently separated saddle node.

Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools

“…The H–H, Rössler, and Hindmarsh–Rose are a kind of universal models that can capture the essential dynamics observed in single neurons, groups of neurons, and brain areas. A comparison of their efficiency in approximating specifics such as spiking, bursting, and chaotic patterning of different types of real neurons can be found in Refs . These specific dynamics can be systematically identified and categorized with the aid of bifurcation analysis …”

Single Gaussian Chaotic Neuron: Numerical Study and Implementation in an Embedded System

“…A comparison between single neurons models is made in the work of Sungar et al [6] to establish a model for cognitive purpose, where synchronization conditions are explored. A neural model with a high propensity to synchronization could be more plausible for modeling neural activity.…”