Correlation-invariant synaptic plasticity

TL;DR

This paper introduces a correlation-invariant synaptic plasticity model that enables cortical neurons to develop sparse, higher-order statistical representations by overcoming the limitations of traditional Hebbian learning's sensitivity to second-order correlations.

Contribution

It presents a novel theory for synaptic plasticity that cancels second-order correlation sensitivity, aligning receptive fields with higher-order features, and demonstrates its biological plausibility.

Findings

Correlation-invariance allows development of sparse population codes.

Synaptic LTD cancels sensitivity to second-order correlations.

Models develop higher-order statistical representations despite variability.

Abstract

Cortical populations of neurons develop sparse representations adapted to the statistics of the environment. While existing synaptic plasticity models reproduce some of the observed receptive-field properties, a major obstacle is the sensitivity of Hebbian learning to omnipresent spurious correlations in cortical networks which can overshadow relevant latent input features. Here we develop a theory for synaptic plasticity that is invariant to second-order correlations in the input. Going beyond classical Hebbian learning, we show how Hebbian long-term depression (LTD) cancels the sensitivity to second-order correlations, so that receptive fields become aligned with features hidden in higher-order statistics. Our simulations demonstrate how correlation-invariance enables biologically realistic models to develop sparse population codes, despite diverse levels of variability and heterogeneity. The theory advances our understanding of local unsupervised learning in cortical circuits and assigns a specific functional role to synaptic LTD mechanisms in pyramidal neurons.