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Sequential Bayesian Decoding with a Population of Neurons
journal contribution
posted on 2023-06-08, 10:09 authored by Si Wu, Danmei Chen, Mahesan Niranjan, Shun-ichi AmariNo description supplied
History
Publication status
- Published
Journal
Neural ComputationISSN
0899-7667External DOI
Issue
5Volume
17Page range
993-1012Pages
16.0Department affiliated with
- Informatics Publications
Notes
Originality: This work for the first time investigated the implementation of Bayesian inference in neural population codes. It was also one of the early studies in the field that explored the application of Bayesian inference in brain functions. Rigor: The work applied a combination of methods, including Information Theory, Statistical Inference and the Theory of Dynamical Systems, to analyze the dynamical behaviours of the neural system. It developed a novel strategy to quantify the decoding performance of the network analytically. Significance: The first paper that gave a concrete proof about the implementation of Bayesian inference in neural circuitry. Impact: This work was developed further by several authors to explore the Bayesian nature of neural information processing. Outlet: Top Neural Computation journal.Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2012-02-06Usage metrics
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