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

Blindfold learning of an accurate neural metric