ArticleProceedings of the National Academy of Sciences of the United States of America2018
Blindfold learning of an accurate neural metric.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The information theory of developmental pruning: Optimizing global network architectures using local synaptic rules.PLoS computational biology · 2021Article
- Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics.Entropy (Basel, Switzerland) · 2020Review
- Optimal Encoding in Stochastic Latent-Variable Models.Entropy (Basel, Switzerland) · 2020Article
- Measures of Neural Similarity.Computational brain & behavior · 2020Article
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Authors and funding
3 authors.
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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 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.
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