Population Decoding Based on an Unfaithful Model

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 calculate the decoding error of UMLI and show an example of an unfaithful model which neglects the neuronal correlation. 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 advantage of decreasing the computational complexity remarkablely and maintaining a high level decoding accuracy at the same time.