Adaptive compressed sensing - a new class of self-organizing coding models for neuroscience

TL;DR

This paper introduces a novel adaptive compressed sensing model for neuroscience that enables biologically realistic receptive fields and cortical communication with limited and distorted sensory input.

Contribution

It proposes a new sparse coding network integrating synaptic learning within compressed sensing, relaxing wiring constraints and enabling meaningful cortical representations.

Findings

Receptive fields remain spatially smooth despite subsampled input.

Model can form representations in secondary sensory areas via cortico-cortical projections.

Recurrent connections are essential for forming receptive fields in the new sparse coding class.

Abstract

Sparse coding networks, which utilize unsupervised learning to maximize coding efficiency, have successfully reproduced response properties found in primary visual cortex \cite{AN:OlshausenField96}. However, conventional sparse coding models require that the coding circuit can fully sample the sensory data in a one-to-one fashion, a requirement not supported by experimental data from the thalamo-cortical projection. To relieve these strict wiring requirements, we propose a sparse coding network constructed by introducing synaptic learning in the framework of compressed sensing. We demonstrate that the new model evolves biologically realistic spatially smooth receptive fields despite the fact that the feedforward connectivity subsamples the input and thus the learning has to rely on an impoverished and distorted account of the original visual data. Further, we demonstrate that the model could form a general scheme of cortical communication: it can form meaningful representations in a secondary sensory area, which receives input from the primary sensory area through a "compressing" cortico-cortical projection. Finally, we prove that our model belongs to a new class of sparse coding algorithms in which recurrent connections are essential in forming the spatial receptive fields.