Drifting states and synchronization induced chaos in autonomous networks of excitable neurons

TL;DR

This paper investigates how autonomous networks of excitatory neurons can exhibit various dynamical states, including chaos, driven by structural variability and refractory periods, revealing mechanisms for asynchronous and synchronized neural activity.

Contribution

It demonstrates that purely excitatory networks can generate autonomous, diverse dynamical states and introduces a novel method for analyzing chaos via interspike interval pairs.

Findings

Isolated excitatory networks show asynchronous firing with minimal structural variability.

Networks exhibit a range of states, including asynchronous drifting, synchronized, and mixed states.

A new chaos analysis tool based on interspike interval pairs reveals fractal attractors.

Abstract

The study of balanced networks of excitatory and inhibitory neurons has led to several open questions. On the one hand it is yet unclear whether the asynchronous state observed in the brain is autonomously generated, or if it results from the interplay between external drivings and internal dynamics. It is also not known, which kind of network variabilities will lead to irregular spiking and which to synchronous firing states. Here we show how isolated networks of purely excitatory neurons generically show asynchronous firing whenever a minimal level of structural variability is present together with a refractory period. Our autonomous networks are composed of excitable units, in the form of leaky integrators spiking only in response to driving inputs. For a non-uniform network, composed exclusively of excitatory neurons, we find a rich repertoire of self-induced dynamical states, including asynchronous drifting states, fully synchronized, or mixed, containing both drifting and synchronized components. The individual neurons considered are excitable and hence do not dispose of intrinsic natural firing frequencies. An effective network-wide distribution of natural frequencies is however generated autonomously through self-consistent feedback loops. We find two types of asynchronous activity, with the individual neurons spiking regularly in the pure drifting state, albeit with a continuous distribution of firing frequencies. The activity of the drifting component, however, becomes irregular in the mixed state, due to the periodic driving of the synchronized component. We propose a new tool for the study of chaos in spiking neural networks, which consists of an analysis of the time series of pairs of consecutive interspike intervals. In this space, we show that a strange attractor with a fractal dimension is formed.