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Understanding why neural systems can process information extremely fast is a fundamental question in theoretical neuroscience. The present study investigates the effect of noise on accelerating neural computation. To evaluate the speed of network response, we consider a computational task in which the network tracks time-varying stimuli. Two noise structures are compared, namely, the stimulus-dependent...
A fundamental question in theoretical neuro-science is to answer why neural systems can process information extremely fast. Here we investigate the effect of noise and neuronal collaborative activity on speeding up population decoding. We consider a one-dimensional stimulus encoded by a number of integrate-and-fire neurons. We find that 1) when noise is Poissionian, i.e., its variance is proportional...
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