Spiking neural network

Overview
Spiking neural networks (SNNs) fall into the third generation of neural network models, increasing the level of realism in a neural simulation. In addition to neuronal and synaptic state, spiking neural networks also incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not fire at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather fire only when a membrane potential - an intrinsic quality of the neuron related to its membrane electrical charge - reaches a specific value. When a neuron fires, it generates a signal which travels to other neurons which, in turn, increase their potentials in accordance with this signal.

In the context of spiking neural networks, the current activation level (modelled as some differential equation) is normally considered to be the neuron's state, with incoming spikes pushing this value higher, and either firing or decay over time pulling it lower. Various coding methods exist for interpreting the outgoing spike train as a real-value number, either relying on the frequency of spikes, or the timing between spikes, to encode information.