Population Coding and Decoding in a Neural Field: A Computational Study
journal contribution
posted on 2023-06-08, 05:35 authored by Si Wu, Shun-ichi Amari, Hiroyuki NakaharaNo description supplied
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Publication status
- Published
Journal
Neural ComputationISSN
0899-7667External DOI
Issue
5Volume
14Page range
999-1026Pages
28.0Department affiliated with
- Informatics Publications
Notes
Originality: This work investigated the performances of neural population coding under different correlation structures, and for the first time, it systematically clarified the conditions under which a population decoding strategy is efficient. Rigor: This work applied a combination of methods, including Information Theory, Statistical Inference and the Theory of Dynamical Systems, to analyze the performance of neural population decoding. It found that when the neuronal correlation is strong, population decoding is not as efficient as many people thought before. Significance: This work developed a new mathematical method to analyze the efficiency of neural population decoding. The results on the efficiency of population decoding have important guidance on data analysis in neurophysiology experiments. Impact: This work has important impact on our understanding of population coding, an important feature of neural information processing. It has citations: Web of Knowledge=11, Google Scholar=18.Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2012-02-06Usage metrics
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