ArticleNature communications2021
Revealing nonlinear neural decoding by analyzing choices.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Distinct neural oscillations predict individual differences in feedback-guided cognitive flexibility.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Broadband synergy versus oscillatory redundancy in the visual cortex.Nature communications · 2026Article
- Population-level encoding of task information is stronger in the adolescent compared to adult frontal cortex.Developmental cognitive neuroscience · 2026Article
- Broadband synergy versus oscillatory redundancy in the visual cortex.bioRxiv : the preprint server for biology · 2025Article
- Area MT carries acceleration information in a quickly and directly decodable representation.bioRxiv : the preprint server for biology · 2025Article
- Representational similarity analysis reveals cue-independent spatial representations for landmarks and self-motion cues in human retrosplenial cortex.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Article
- Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics.Scientific reports · 2024Article
- The computational rules of cross-modality suppression in the visual posterior sylvian area.iScience · 2023Article
- A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions.Nature communications · 2023Article
- Article
- Rational thoughts in neural codes.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
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Authors and funding
5 authors.
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Abstract
Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, describing redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. This relationship holds for optimal feedforward networks of modest complexity, when experiments are performed under natural nuisance variation. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding.
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