Self-organization using synaptic plasticity

TL;DR

This paper demonstrates how networks of spiking neurons can self-organize to a critical state with maximal dynamic range through locally derived synaptic plasticity rules, resembling phase transitions in physics.

Contribution

It introduces a novel local synaptic plasticity rule that enables neural networks to self-organize to a critical state, optimizing their dynamic range.

Findings

Networks reach a critical state with maximal dynamic range.

Synaptic plasticity rules are derived analytically.

Global homeostasis is achieved through local regulation.

Abstract

Large networks of spiking neurons show abrupt changes in their collective dynamics resembling phase transitions studied in statistical physics. An example of this phenomenon is the transition from irregular, noise-driven dynamics to regular, self-sustained behavior observed in networks of integrate-and-fire neurons as the interaction strength between the neurons increases. In this work we show how a network of spiking neurons is able to self-organize towards a critical state for which the range of possible inter-spike-intervals (dynamic range) is maximized. Self-organization occurs via synaptic dynamics that we analytically derive. The resulting plasticity rule is defined locally so that global homeostasis near the critical state is achieved by local regulation of individual synapses.