Efficient event-driven simulation of large networks of spiking neurons anddynamical synapses

A simulation procedure is described for making feasible large-scale simulat ions of recurrent neural networks of spiking neurons and plastic synapses. The procedure is applicable if the dynamic variables of both neurons and sy napses evolve deterministically between any two successive spikes. Spikes i ntroduce jumps in these variables, and since spike trains are typically noi sy, spikes introduce stochasticity into both dynamics. Since all events in the simulation are guided by the arrival of spikes, at neurons or synapses, we name this procedure event-driven. The procedure is described in detail, and its logic and performance are com pared with conventional (synchronous) simulations. The main impact of the n ew approach is a drastic reduction of the computational load incurred upon introduction of dynamic synaptic efficacies, which vary organically as a fu nction of the activities of the pre- and postsynaptic neurons. In fact, the computational load per neuron in the presence of the synaptic dynamics gro ws linearly with the number of neurons and is only about 6% more than the l oad with fixed synapses. Even the latter is handled quite efficiently by th e algorithm. We illustrate the operation of the algorithm in a specific case with integr ate-and-fire neurons and specific spike-driven synaptic dynamics. Both dyna mical elements have been found to be naturally implementable in VLSI. This network is simulated to show the effects on the synaptic structure of the p resentation of stimuli, as well as the stability of the generated matrix to the neural activity it induces.