A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields

TL;DR

This paper demonstrates that a biologically plausible spiking neural network with local plasticity rules can learn to produce the diverse receptive fields of V1 simple cells from natural images, bridging theoretical models and biological realism.

Contribution

It introduces a biologically realistic spiking network model that learns diverse V1 receptive fields using only local synaptic information, a first in the field.

Findings

The model accurately predicts the diversity of V1 simple cell receptive fields.

Sparse coding principles with local plasticity can optimize a generative image model.

The network exhibits emergent properties aligning with experimental observations.

Abstract

Sparse coding algorithms trained on natural images can accurately predict the features that excite visual cortical neurons, but it is not known whether such codes can be learned using biologically realistic plasticity rules. We have developed a biophysically motivated spiking network, relying solely on synaptically local information, that can predict the full diversity of V1 simple cell receptive field shapes when trained on natural images. This represents the first demonstration that sparse coding principles, operating within the constraints imposed by cortical architecture, can successfully reproduce these receptive fields. We further prove, mathematically, that sparseness and decorrelation are the key ingredients that allow for synaptically local plasticity rules to optimize a cooperative, linear generative image model formed by the neural representation. Finally, we discuss several interesting emergent properties of our network, with the intent of bridging the gap between theoretical and experimental studies of visual cortex.