Intrinsic adaptation in autonomous recurrent neural networks

TL;DR

This paper investigates how intrinsic, non-synaptic plasticity influences the autonomous activity of recurrent neural networks, revealing three distinct dynamical regimes and their implications for self-organized information processing.

Contribution

It introduces a model of intrinsic adaptation acting on neural parameters, demonstrating its role in generating diverse dynamical states in recurrent neural networks.

Findings

Identification of three dynamical regimes: synchronized, chaotic, and bursting.

Intermittent bursting regime responds selectively to external stimuli.

Intrinsic adaptation leads to self-organized critical dynamics.

Abstract

A massively recurrent neural network responds on one side to input stimuli and is autonomously active, on the other side, in the absence of sensory inputs. Stimuli and information processing depends crucially on the qualia of the autonomous-state dynamics of the ongoing neural activity. This default neural activity may be dynamically structured in time and space, showing regular, synchronized, bursting or chaotic activity patterns. We study the influence of non-synaptic plasticity on the default dynamical state of recurrent neural networks. The non-synaptic adaption considered acts on intrinsic neural parameters, such as the threshold and the gain, and is driven by the optimization of the information entropy. We observe, in the presence of the intrinsic adaptation processes, three distinct and globally attracting dynamical regimes, a regular synchronized, an overall chaotic and an intermittent bursting regime. The intermittent bursting regime is characterized by intervals of regular flows, which are quite insensitive to external stimuli, interseeded by chaotic bursts which respond sensitively to input signals. We discuss these finding in the context of self-organized information processing and critical brain dynamics.