Spiking neural network

Uncommon card · No. 196983

Spiking neural networks (SNNs) are artificial neural networks (ANN) that mimic natural neural networks. These models leverage timing of discrete spikes as the main information carrier. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential—an intrinsic quality of the neuron related to its membrane electrical charge—reaches a specific value, called the threshold. As a neuron integrates its inputs over time, its membrane potential increases or decreases; once it reaches the threshold, the neuron fires and generates a signal that travels to other neurons, which in turn increase or decrease their own potentials in response. A neuron model that fires at the moment of threshold crossing is also called a spiking neuron model. While...

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