Blindfold learning of an accurate neural metric

Abstract

<p id="d7621050e196">To understand how neural signals code sensory stimuli, most approaches require knowing both the true stimulus and the neural response. The brain, however, only has access to the neural signals put out by sensory organs. How can it learn to relate neural responses to sensory stimuli, especially for responses to which it has never been exposed? Here we show how to solve this problem by building a metric on neural responses such that responses to the same stimulus are close. Although the metric is built with no access to the stimulus, it outperforms all existing metrics in fine discrimination tasks, suggesting a way the brain could make sense of its sensory output. </p><p class="first" id="d7621050e199">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 learn representations of the stimuli based on those noisy, correlated responses alone. Here we show how to build an accurate distance map of responses solely from the structure of the population activity of retinal ganglion cells. 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. </p>