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Abstract:
This study investigates a population decoding paradigm, in
which the estimation of stimulus in the previous step is used as
prior knowledge for consecutive decoding. We analyze the decoding
accuracy of such a Bayesian decoder (Maximum a Posteriori
Estimate), and show that it can be implemented by a biologically
plausible recurrent network, where the prior knowledge of
stimulus is conveyed by the change in recurrent interactions as a
result of Hebbian learning.
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