Adaptive self-organization in a realistic neural network model

TL;DR

This paper demonstrates that spike-time-dependent plasticity can self-organize neural networks toward criticality, aligning with empirical data and highlighting the importance of dynamics and topology in neural information processing.

Contribution

It introduces a realistic neural network model showing how synaptic plasticity drives networks to critical states, extending prior theoretical mechanisms to biological neural systems.

Findings

Model reproduces empirical synaptic strength distributions.

Networks self-organize near criticality through plasticity.

Predictions relate synaptic distributions to critical states.

Abstract

Information processing in complex systems is often found to be maximally efficient close to critical states associated with phase transitions. It is therefore conceivable that also neural information processing operates close to criticality. This is further supported by the observation of power-law distributions, which are a hallmark of phase transitions. An important open question is how neural networks could remain close to a critical point while undergoing a continual change in the course of development, adaptation, learning, and more. An influential contribution was made by Bornholdt and Rohlf, introducing a generic mechanism of robust self-organized criticality in adaptive networks. Here, we address the question whether this mechanism is relevant for real neural networks. We show in a realistic model that spike-time-dependent synaptic plasticity can self-organize neural networks robustly toward criticality. Our model reproduces several empirical observations and makes testable predictions on the distribution of synaptic strength, relating them to the critical state of the network. These results suggest that the interplay between dynamics and topology may be essential for neural information processing.