The basic structure of an artificial neuron corresponds to that of its biological counterpart and is replicated in the form of software modules:
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An artificial neuron receives input information via connections to other neurons with corresponding input values Ok, where k = 1..n.
The influence of these input values is modelled by n real numbers, referred to as input weights Wkj (corresponding to synapses in the biological world).
The propagation functionj combines the inputs with the weights and aggregates the overall information. The activation function determines the new activity aj using a threshold θj. The output function fout determines the output value oj from the neuron’s activity aj.
