Critical drift in a neuro-inspired adaptive network

TL;DR

This paper demonstrates that neuro-inspired networks can drift along a high-dimensional critical manifold, maintaining criticality through homeostatic plasticity, which offers insights into brain dynamics and adaptive critical states.

Contribution

It introduces a model showing how adaptation rules lead to high-dimensional critical drift, extending the concept of criticality beyond one-dimensional parameter tuning.

Findings

Networks drift on a critical manifold while parameters change

Homeostatic plasticity maintains criticality during drift

Critical states occupy a high-dimensional parameter space

Abstract

It has been postulated that the brain operates in a self-organized critical state that brings multiple benefits, such as optimal sensitivity to input. Thus far, self-organized criticality has typically been depicted as a one-dimensional process, where one parameter is tuned to a critical value. However, the number of adjustable parameters in the brain is vast, and hence critical states can be expected to occupy a high-dimensional manifold inside a high-dimensional parameter space. Here, we show that adaptation rules inspired by homeostatic plasticity drive a neuro-inspired network to drift on a critical manifold, where the system is poised between inactivity and persistent activity. During the drift, global network parameters continue to change while the system remains at criticality.