Population Decoding Based on an Unfaithful Model

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

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Si Wu, Hiroyuki Nakahara, Noboru Murata, Shun-ichi Amari


We study a population decoding paradigm in which the maximum likeli(cid:173) hood 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 de(cid:173) coding 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 effi(cid:173) cient 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 de(cid:173) coding 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.