Population coding with correlation and an unfaithful model

This study investigates 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 pr ocess of the brain is not exactly known or because a simplified decoding mo del is preferred for saving computational cost. We consider an unfaithful d ecoding model that neglects the pair-wise correlation between neuronal acti vities and prove that UMLI is asymptotically efficient when the neuronal co rrelation is uniform or of limited range. The performance of UMLI is compar ed with that of the maximum likelihood inference based on the faithful mode l and that of the center-of-mass decoding method. It turns out that UMLI ha s advantages of decreasing the computational complexity remarkably and main taining high-level decoding accuracy Moreover, it fan be implemented by a b iologically feasible recurrent network (Pouget, Zhang, Deneve, & Latham, 19 98). The effect of correlation on the decoding accuracy is also discussed.