Population decoding based on an unfaithful model

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

研究成果: Conference contribution

10 被引用数 (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.

ホスト出版物のタイトルAdvances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999
出版社Neural information processing systems foundation
ISBN(印刷版)0262194503, 9780262194501
出版ステータスPublished - 2000 1 1
イベント13th Annual Neural Information Processing Systems Conference, NIPS 1999 - Denver, CO, United States
継続期間: 1999 11 291999 12 4


名前Advances in Neural Information Processing Systems


Conference13th Annual Neural Information Processing Systems Conference, NIPS 1999
CountryUnited States
CityDenver, CO

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

フィンガープリント 「Population decoding based on an unfaithful model」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。