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Population coding with correlation and an unfaithful model
journal contributionposted on 2023-06-07, 22:40 authored by Si Wu, Hiroyuki Nakahara, Shun-ichi Amari
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- Informatics Publications
NotesOriginality: Proposed for the first time the important concept of unfaithful neural decoding, which captures the fact that in reality the brain has to read out information based on incomplete knowledge about the external world. Rigor: Developed a new statistical inference method to analyze the performance of neural estimators in different scenario of unfaithful decoding, and observed that in many cases the neural correlation can be neglected in population decoding. Significance: This work has raised to the field the important concept of unfaithful neural decoding, and developed a method to quantify its performances. Impact: This work has contributed to open up a new sub-area in Computational Neuroscience for exploring the effect of correlation on neural coding. Citations excluding self-ones: Web of Knowledge=18, Google Scholar=20.
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