Before revision:  We show that biphasic synaptic events do yield very sharp post synaptic currents that maintain good separation of homogeneity responses in massively noisy environments. The same technique opens up a neural method of tuning neurons to varying degrees of homogeneity. The simple neural implementation, generality, robustness, and variable tuning make homogeneity-detection an attractive scheme of computation in spiking neural networks and in the brain .
After revision:  We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic postsynaptic events. The delay of the feed-forward inhibition allows effectively controlling the size of the temporal integration window and, thereby, the coincidence threshold. The proposed model is based on single-spike latency codes in a purely feed-forward architecture that supports low-latency processing, making it an attractive scheme of computation in spiking neural networks and in the brain .