Self-organization toward criticality by synaptic plasticity

TL;DR

This review explores how biologically plausible synaptic plasticity rules can self-organize neural networks toward criticality, shedding light on brain function and the emergence of scale-free dynamics.

Contribution

It categorizes plasticity rules based on their ability to sustain criticality and discusses their role in neural computation and self-organization.

Findings

Plasticity rules can stabilize or destabilize criticality in neural networks.

Some rules maintain near-critical dynamics even after being disabled.

Criticality plays a significant role in neural computation and self-organization.

Abstract

Self-organized criticality has been proposed to be a universal mechanism for the emergence of scale-free dynamics in many complex systems, and possibly in the brain. While such scale-free patterns were identified experimentally in many different types of neural recordings, the biological principles behind their emergence remained unknown. Utilizing different network models and motivated by experimental observations, synaptic plasticity was proposed as a possible mechanism to self-organize brain dynamics towards a critical point. In this review, we discuss how various biologically plausible plasticity rules operating across multiple timescales are implemented in the models and how they alter the network's dynamical state through modification of number and strength of the connections between the neurons. Some of these rules help to stabilize criticality, some need additional mechanisms to prevent divergence from the critical state. We propose that rules that are capable of bringing the network to criticality can be classified by how long the near-critical dynamics persists after their disabling. Finally, we discuss the role of self-organization and criticality in computation. Overall, the concept of criticality helps to shed light on brain function and self-organization, yet the overall dynamics of living neural networks seem to harnesses not only criticality for computation, but also deviations thereof.