Transient synchrony and the integration of spectrotemporal information

In contrast to networks of neurons where behavior is governed by average firing rate, what computations are implemented most easily, efficiently, and robustly by networks of neurons that spike? Spiking neurons synchronize much more readily when their firing rates are similar than when they are different. This property can be used to very simply and robustly implement, in a network of appropriately connected spiking neurons, a "many are equals" operation: Synchronization indicates that many of the neurons' firing rates are similar. Such an operation is computationally very powerful. The computation is robust to outliers, and contains a natural invariance: over a broad range of firing rates, the synchronization phenomenon depends only on rate similarity, and not on the precise firing rate level. We demonstrate the computational power of this operation by constructing a simple network of spiking neurons with output neurons that respond selectively to a complex spectrotemporal pattern, the spoken word "one". The response is invariant to uniform time-warp. Time is encoded by slowly decaying firing rates, and the selectivity is largely speaker-independent. We posit "many are equals" synchronization is a simple yet powerful computational building block for spiking neural networks.