Event-driven simulation of continuous-time neural networks

The transforming of incoming signals into action potentials by neurons is believed to be the basis for information processing in nervous systems. In many cases, the accurate representation of involved timings variability is necessary for a correct computation in neural network simulations. A lot of nervous system simulations reported in scientific literature are computed with time-step based methods. This technique is valid to describe many aspects of biological circuits, but some computational aspects (inefficiency, unreliability, etc.) have been highlighted when used in certain scenarios, especially on very large nets. In this work, a very simple and effective analog spiking neural network simulator, based on LIF (Leaky Integrate and Fire) with latency neurons, is presented. It is simulated with an event-driven method, necessary to guarantee the preservation of the original process behavior. In this way, the simulation proceeds without any forcing in order to obtain a compromise between high precision and computational cost. Networks with up to 105 neurons for more than 105 spikes, can be simulated in a few minutes (using a standard PC) with a simple MATLAB tool. Plasticity algorithms are also applied to develop bio-inspired applications and emulate interesting global effects as the Neuronal Group Selection.