The ideal noisy environment for fast neural computation

A central issue in computational neuroscience is to answer why neural systems can process information extremely fast. Here we investigate the effect of noise and the collaborative activity of a neural population on speeding up computation. We find that 1) when input noise is Poissonian, i.e., its variance is proportional to the mean, and 2) when the neural ensemble is initially at its stochastic equilibrium state, noise has the 'best' effect of accelerating computation, in the sense that the input strength 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 read out the stimulus rapidly and accurately.