On the condition for fast neural computation

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 to the mean, and 2) when a neural ensemble is at its stochastic equilibrium state, noise has the `best´ effect of accelerating computation, in the sense that the strength of external inputs is linearly encoded by the number of neurons firing in a short-time window, and that the neural system can use a simple strategy to decode the input rapidly and accurately. Interestingly, we also observe that under this noisy environment, the accuracy of neural decoding in short-time window is insensitive to the noise strength.