Feedback inhibition shapes emergent computational properties of cortical microcircuit motifs

TL;DR

This paper investigates how feedback inhibition in cortical microcircuits, shaped by spike-timing dependent plasticity, enables the emergence of computational functions like pattern disentanglement and sparse coding.

Contribution

It demonstrates that data-based feedback inhibition contributes to emergent computational properties in cortical motifs, specifically pattern separation and sparse coding.

Findings

Feedback inhibition facilitates pattern disentanglement.

STDP shapes inhibitory-excitatory interactions.

Microcircuit motifs support sparse assembly coding.

Abstract

Cortical microcircuits are very complex networks, but they are composed of a relatively small number of stereotypical motifs. Hence one strategy for throwing light on the computational function of cortical microcircuits is to analyze emergent computational properties of these stereotypical microcircuit motifs. We are addressing here the question how spike-timing dependent plasticity (STDP) shapes the computational properties of one motif that has frequently been studied experimentally: interconnected populations of pyramidal cells and parvalbumin-positive inhibitory cells in layer 2/3. Experimental studies suggest that these inhibitory neurons exert some form of divisive inhibition on the pyramidal cells. We show that this data-based form of feedback inhibition, which is softer than that of winner-take-all models that are commonly considered in theoretical analyses, contributes to the emergence of an important computational function through STDP: The capability to disentangle superimposed firing patterns in upstream networks, and to represent their information content through a sparse assembly code.