Agreement in Spiking Neural Networks.

We study the problem of binary agreement in a spiking neural network (SNN). We show that binary agreement on n inputs can be achieved with O ( n ) of auxiliary neurons. Our simulation results suggest that agreement can be achieved in our network in O ( log n ) time. We then describe a subclass of SNNs with a biologically plausible property, which we call size-independence. We prove that solving a class of problems, including agreement and Winner-Take-All, in this model requires Ω ( n ) auxiliary neurons, which makes our agreement network size-optimal.