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Article Dans Une Revue Proceedings of the National Academy of Sciences of the United States of America Année : 2018

Blindfold learning of an accurate neural metric

Résumé

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.

Dates et versions

hal-03897085 , version 1 (13-12-2022)

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Citer

Christophe Gardella, Olivier Marre, Thierry Mora. Blindfold learning of an accurate neural metric. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115 (13), pp.3267-3272. ⟨10.1073/pnas.1718710115⟩. ⟨hal-03897085⟩
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