Poincare return maps in neural dynamics: three examples

TL;DR

This paper introduces new computational tools for analyzing transitions between activity patterns in neural systems, demonstrated through three examples of slow-fast neural models.

Contribution

It presents novel computational methods for studying neural dynamics, applied to three different slow-fast neural systems.

Findings

New tools effectively analyze neural activity transitions

Demonstrated across three neural system examples

Enhances understanding of neural pattern changes

Abstract

Understanding of the onset and generic mechanisms of transitions between distinct patterns of activity in realistic models of individual neurons and neural networks presents a fundamental challenge for the theory of applied dynamical systems. We use three examples of slow-fast neural systems to demonstrate a suite of new computational tools to study diverse neuronal systems.