Spiking neural network
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[edit] 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, compared with MLPs, there is no clear information regarding what can be considered the neuron's value, since neurons are assumed to be aware only of spike information, and nothing else. Various coding methods exist for getting the value of the neuron in question.
