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Spiking neural networks based on LIF with latency: Simulation and synchronization effects

In this paper, a work on spiking neural networks based on a model of a kind of Leaky Integrate-and-Fire (LIF) neuron with latency is presented. Efficient simulations are carried out through an ad hoc event-driven approach, highlighting some particular effects of synchrony in a simple feedforward network topology. These results are consistent with literature results and, thanks to the implementation of the biologically plausible latency effect in the model, new results have emerged from the simulations. The authors plan to apply these results in the near future to applications in which this kind of neural networks and Digital Signal Processing (DSP) applications can be merged to obtain powerful nonlinear DSP techniques. In the plan of the authors is also the definition of a hardware prototype of the network based on analog/digital techniques.