Sequential Bayesian Decoding with a Population of Neurons

Wu, Si, Chen, Danmei, Niranjan, Mahesan and Amari, Shun-ichi (2003) Sequential Bayesian Decoding with a Population of Neurons. Neural Computation, 17 (5). pp. 993-1012. ISSN 0899-7667

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Item Type: Article
Additional Information: 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.
Schools and Departments: School of Engineering and Informatics > Informatics
Depositing User: Si Wu
Date Deposited: 06 Feb 2012 21:29
Last Modified: 28 Mar 2012 13:51
URI: http://sro.sussex.ac.uk/id/eprint/31477
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