The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.
I NFERENCE AND C OMPUTATION WITH P OPULATION C ODES
Abstract
▪ Abstract In the vertebrate nervous system, sensory stimuli are typically encoded through the concerted activity of large populations of neurons. Classically, these patterns of activity have been treated as encoding the value of the stimulus (e.g., the orientation of a contour), and computation has been formalized in terms of function approximation. More recently, there have been several suggestions that neural computation is akin to a Bayesian inference process, with population activity patterns representing uncertainty about stimuli in the form of probability distributions (e.g., the probability density function over the orientation of a contour). This paper reviews both approaches, with a particular emphasis on the latter, which we see as a very promising framework for future modeling and experimental work.
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Cites
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Metadata
- Authors
- Pouget, A., Dayan, P., & Zemel, R. S.
- Venue
- Annual Review of Neuroscience
- Publisher
- Annual Reviews
- Published
- 2003-03
- Volume
- 26
- Issue
- 1
- Pages
- 381-410
- ISSN
- 0147-006X (ISSN-L)
- 1545-4126
- OpenAlex
- W2127806105
- Cited by
- 434
- References
- 96
- FWCI
- 1.98
- Type
- journal article
- Language
- en
- Open access
- closed
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- Pouget, A., Dayan, P., & Zemel, R. S. (2003). I NFERENCE AND C OMPUTATION WITH P OPULATION C ODES. Annual Review of Neuroscience. https://doi.org/10.1146/annurev.neuro.26.041002.131112
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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