ReviewNature neuroscience2023
Bayesian encoding and decoding as distinct perspectives on neural coding.
Review in Nature neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Identifiability of Bayesian models of perception.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Organization, neuronal composition, and dynamics of neuronal ensembles in the songbird auditory forebrain.iScience · 2026Article
- Effort and its perception revisited: How physical-domain insights could lead toward a unified theory.Cognitive, affective & behavioral neuroscience · 2026Review
- Top-down perceptual inference shaping the activity of early visual cortex.Nature communications · 2025Article
- Brain-like variational inference.ArXiv · 2025Article
- The effects of the post-delay epochs on working memory error reduction.PLoS computational biology · 2025Article
- FrAMBI: A Software Framework for Auditory Modeling Based on Bayesian Inference.Neuroinformatics · 2025Article
- Next-Generation Neurotechnologies Inspired by Motor Primitive Model for Restoring Human Natural Movement.Research (Washington, D.C.) · 2025Review
- Review
- Jointly efficient encoding and decoding in neural populations.PLoS computational biology · 2024Article
- How does V1 population activity inform perceptual certainty?Journal of vision · 2024Article
- Through their eyes: Multi-subject brain decoding with simple alignment techniques.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Article
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Authors and funding
4 authors.
Funding
Abstract
The Bayesian brain hypothesis is one of the most influential ideas in neuroscience. However, unstated differences in how Bayesian ideas are operationalized make it difficult to draw general conclusions about how Bayesian computations map onto neural circuits. Here, we identify one such unstated difference: some theories ask how neural circuits could recover information about the world from sensory neural activity (Bayesian decoding), whereas others ask how neural circuits could implement inference in an internal model (Bayesian encoding). These two approaches require profoundly different assumptions and lead to different interpretations of empirical data. We contrast them in terms of motivations, empirical support and relationship to neural data. We also use a simple model to argue that encoding and decoding models are complementary rather than competing. Appreciating the distinction between Bayesian encoding and Bayesian decoding will help to organize future work and enable stronger empirical tests about the nature of inference in the brain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.