Microsoft Azure, page-287

[0021] A “neuromorphic model” as described herein is a spiking neuron model comprised of a plurality of neurons that are connected via synapses. Each neuron is associated with a threshold value, a weight value, and a refractory period value. A threshold value associated with a neuron is a charge value that must be met or exceeded via input synapses for the neuron to fire. A weight value associated with a neuron is a charge weight that is passed via firing of a first neuron to a second neuron via a connecting synapse. A refractory period value is a duration of time that a neuron may accumulate charge without firing. In neuromorphic models, there is an explicit time component, and rather than holding values, neurons hold charge. Synapses transmit charge over time, and when a synapse’s charge arrives at a neuron, it is added to the neuron’s charge value. If a neuron’s charge exceeds a threshold, the neuron fires, resetting its charge value to some base value and sending charge out along its outgoing synapses. In a neuromorphic model, threshold values, weight values, and refractory period values are configurable. The goal of training a neuromorphic model is to define connections, thresholds, weights and delays so that the model can “solve” a task. With a classification task, the values of the datapoints must be converted into spikes, and output spikes must be converted into classifications.