Resonances induced by Spiking Time Dependent Plasticity

TL;DR

This paper investigates how neural populations learn to respond better to periodic stimuli using Spiking Time Dependent Plasticity and homeostatic mechanisms, revealing resonance phenomena.

Contribution

It introduces a theoretical framework combining Differential Hebbian Learning and homeostasis to explain stimulus resonance in spiking neural networks.

Findings

Resonance phenomena emerge in neural responses to periodic stimuli.

Theoretical equations predict increased stimulus response due to plasticity.

Results are consistent with rate and population coding interpretations.

Abstract

Neural populations exposed to a certain stimulus learn to represent it better. However, the process that leads local, self-organized rules to do so is unclear. We address the question of how can a neural periodic input be learned and use the Differential Hebbian Learning framework, coupled with a homeostatic mechanism to derive two self-consistency equations that lead to increased responses to the same stimulus. Although all our simulations are done with simple Leaky-Integrate and Fire neurons and standard Spiking Time Dependent Plasticity learning rules, our results can be easily interpreted in terms of rates and population codes.