Population decoding based on an unfaithful model

S. Wu, H. Nakahara, N. Murata, S. Amari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


We study a population decoding paradigm in which the maximum likelihood inference is based on an unfaithful decoding model (UMLI). This is usually the case for neural population decoding because the encoding process of the brain is not exactly known, or because a simplified decoding model is preferred for saving computational cost. We consider an unfaithful decoding model which neglects the pair-wise correlation between neuronal activities, and prove that UMLI is asymptotically efficient when the neuronal correlation is uniform or of limited-range. The performance of UMLI is compared with that of the maximum likelihood inference based on a faithful model and that of the center of mass decoding method. It turns out that UMLI has advantages of decreasing the computational complexity remarkablely and maintaining a high-level decoding accuracy at the same time. The effect of correlation on the decoding accuracy is also discussed.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999
PublisherNeural information processing systems foundation
Number of pages7
ISBN (Print)0262194503, 9780262194501
Publication statusPublished - 2000
Externally publishedYes
Event13th Annual Neural Information Processing Systems Conference, NIPS 1999 - Denver, CO, United States
Duration: 1999 Nov 291999 Dec 4

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Conference13th Annual Neural Information Processing Systems Conference, NIPS 1999
Country/TerritoryUnited States
CityDenver, CO

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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