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There is mounting experimental evidence that brain-state specific neuralmechanisms supported by connectomic architectures serve to combine past andcontextual knowledge with current, incoming flow of evidence (e.g. from sensorysystems). Such mechanisms are distributed across multiple spatial and temporalscales and require dedicated support at the levels of individual neurons andsynapses. A prominent feature in the neocortex is the structure of large, deeppyramidal neurons which show a peculiar separation between an apical dendriticcompartment and a basal dentritic/peri-somatic compartment, with distinctivepatterns of incoming connections and brain-state specific activationmechanisms, namely apical-amplification, -isolation and -drive associated tothe wakefulness, deeper NREM sleep stages and REM sleep. The cognitive roles ofapical mechanisms have been demonstrated in behaving animals. In contrast,classical models of learning spiking networks are based on single compartmentneurons that miss the description of mechanisms to combine apical andbasal/somatic information. This work aims to provide the computationalcommunity with a two-compartment spiking neuron model which includes featuresthat are essential for supporting brain-state specific learning and with apiece-wise linear transfer function (ThetaPlanes) at highest abstraction levelto be used in large scale bio-inspired artificial intelligence systems. Amachine learning algorithm, constrained by a set of fitness functions, selectedthe parameters defining neurons expressing the desired apical mechanisms.

Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes