中央研究院 資訊科學研究所

In this talk, we consider networks of deterministic spiking neurons, firing synchronously at discrete times; such spiking neural networks are inspired by networks of neurons and synapses that occur in brains. We consider the problem of translating temporal information into spatial information in such networks, an important task that is carried out by actual brains. Specifically, we define two problems:  ``First Consecutive Spikes Counting (FCSC)" and ``Total Spikes Counting (TSC)", which model spike and rate coding aspects of translating temporal information into spatial information respectively. Assuming an upper bound of $T$ on the length of the temporal input signal, we design two networks that solve these two problems, each using $O([@BackSlash]log T)$ neurons and terminating in time $T+1$. We also prove that these bounds are tight in both time and number of neurons.