The classical model of signal integration in both biological and Artificial Neural Networks looks something like this,

where is some linear or non-linear output function whose parameters adapt to feedback from the outside world through changes to protein dense structures near to the point of signal input, namely the Post Synaptic Density (PSD). In this simple model integration is implied to occur at the soma (cell body) where the input signals are combined and broadcast to other neurons through downstream synapses via the axon. Generally speaking neurons (both artificial and otherwise) exist in multilayer networks composing the inputs of one neuron with the outputs of the others creating cross-linked chains of computation that have been shown to be universal in their ability to approximate any desired input-output behaviour.

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Models of learning and memory have relied heavily on modifications to the PSD to explain modifications in behaviour. Physically these changes result from alterations in the concentration and density of the neurotransmitter receptors and ion channels that occur in abundance at the PSD, but, in actuality these channels occur all along the cell wall of the dendrite on which the PSD is located. Dendrites are something like a multi-branched mass of input connections belonging to each neuron. This begs the question as to whether learning might in fact occur all along the length of each densely branched dendritic tree.

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