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Abstract:
the maximum likelihood 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 model is preferred for saving
computational cost. We consider an unfaithful decoding model which
neglects the pair-wise correlation between neuronal activities. The
decoding error of UMLI is proved to be asymptotically efficient
when the neuronal correlation is uniform or of limited-range. The
performance of UNMLI 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 remarkably
and maintaining a high-level decoding accuracy at the same
time.
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