Blindfold learning of an accurate neural metric

TL;DR

This paper introduces a biologically plausible neural metric learned directly from retinal responses, enabling accurate discrimination of visual stimuli without prior stimulus knowledge.

Contribution

The authors develop the Temporal Restricted Boltzmann Machine to learn neural response structure and define a new spike train distance outperforming existing metrics.

Findings

The new metric accurately discriminates similar stimuli.

It outperforms existing neural distances.

The method is biologically plausible and stimulus-agnostic.

Abstract

The brain has no direct access to physical stimuli, but only to the spiking activity evoked in sensory organs. It is unclear how the brain can structure its representation of the world based on differences between those noisy, correlated responses alone. Here we show how to build a distance map of responses from the structure of the population activity of retinal ganglion cells, allowing for the accurate discrimination of distinct visual stimuli from the retinal response. We introduce the Temporal Restricted Boltzmann Machine to learn the spatiotemporal structure of the population activity, and use this model to define a distance between spike trains. We show that this metric outperforms existing neural distances at discriminating pairs of stimuli that are barely distinguishable. The proposed method provides a generic and biologically plausible way to learn to associate similar stimuli based on their spiking responses, without any other knowledge of these stimuli.