Manifold Transform by Recurrent Cortical Circuit Enhances Robust Encoding of Familiar Stimuli

TL;DR

This paper demonstrates that recurrent cortical circuits shaped by Hebbian learning can enhance the robustness and discrimination of familiar visual stimuli by transforming neural response manifolds, supported by computational and preliminary physiological evidence.

Contribution

It introduces a circuit model that reproduces neural sparsification and robustness to noise for familiar stimuli, revealing the computational role of recurrent dynamics in early visual cortex.

Findings

Recurrent circuits sharpen neural tuning for familiar stimuli.

Familiarization leads to more noise-robust neural representations.

Recurrent computation transforms neural response manifolds for better discrimination.

Abstract

A ubiquitous phenomenon observed throughout the primate hierarchical visual system is the sparsification of the neural representation of visual stimuli as a result of familiarization by repeated exposure, manifested as the sharpening of the population tuning curves and suppression of neural responses at the population level. In this work, we investigated the computational implications and circuit mechanisms underlying these neurophysiological observations in an early visual cortical circuit model. We found that such a recurrent neural circuit, shaped by BCM Hebbian learning, can also reproduce these phenomena. The resulting circuit became more robust against noises in encoding the familiar stimuli. Analysis of the geometry of the neural response manifold revealed that recurrent computation and familiar learning transform the response manifold and the neural dynamics, resulting in enhanced robustness against noise and better stimulus discrimination. This prediction is supported by preliminary physiological evidence. Familiarity training increases the alignment of the slow modes of network dynamics with the invariant features of the learned images. These findings revealed how these rapid plasticity mechanisms can improve contextual visual processing in even the early visual areas in the hierarchical visual system.