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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 Amari
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History

Publication status

  • Published

Journal

Neural Computation

ISSN

0899-7667

Issue

5

Volume

17

Page range

993-1012

Pages

16.0

Department 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

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Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

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