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001022258 0247_ $$2doi$$a10.48550/arXiv.2311.06074
001022258 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-01376
001022258 037__ $$aFZJ-2024-01376
001022258 1001_ $$0P:(DE-HGF)0$$aPastorelli, Elena$$b0$$eCorresponding author
001022258 245__ $$aTwo-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes
001022258 260__ $$barXiv$$c2023
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001022258 520__ $$aThere is mounting experimental evidence that brain-state specific neural mechanisms supported by connectomic architectures serve to combine past and contextual knowledge with current, incoming flow of evidence (e.g. from sensory systems). Such mechanisms are distributed across multiple spatial and temporal scales and require dedicated support at the levels of individual neurons and synapses. A prominent feature in the neocortex is the structure of large, deep pyramidal neurons which show a peculiar separation between an apical dendritic compartment and a basal dentritic/peri-somatic compartment, with distinctive patterns of incoming connections and brain-state specific activation mechanisms, namely apical-amplification, -isolation and -drive associated to the wakefulness, deeper NREM sleep stages and REM sleep. The cognitive roles of apical mechanisms have been demonstrated in behaving animals. In contrast, classical models of learning spiking networks are based on single compartment neurons that miss the description of mechanisms to combine apical and basal/somatic information. This work aims to provide the computational community with a two-compartment spiking neuron model which includes features that are essential for supporting brain-state specific learning and with a piece-wise linear transfer function (ThetaPlanes) at highest abstraction level to be used in large scale bio-inspired artificial intelligence systems. A machine learning algorithm, constrained by a set of fitness functions, selected the parameters defining neurons expressing the desired apical mechanisms.
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001022258 650_7 $$2Other$$aNeurons and Cognition (q-bio.NC)
001022258 650_7 $$2Other$$aNeural and Evolutionary Computing (cs.NE)
001022258 650_7 $$2Other$$aFOS: Biological sciences
001022258 650_7 $$2Other$$aFOS: Computer and information sciences
001022258 7001_ $$0P:(DE-Juel1)161462$$aYegenoglu, Alper$$b1$$ufzj
001022258 7001_ $$0P:(DE-HGF)0$$aKolodziej, Nicole$$b2
001022258 7001_ $$0P:(DE-Juel1)186881$$aWybo, Willem$$b3$$ufzj
001022258 7001_ $$0P:(DE-HGF)0$$aSimula, Francesco$$b4
001022258 7001_ $$0P:(DE-Juel1)165859$$aDiaz, Sandra$$b5$$ufzj
001022258 7001_ $$0P:(DE-HGF)0$$aStorm, Johan Frederik$$b6
001022258 7001_ $$0P:(DE-HGF)0$$aPaolucci, Pier Stanislao$$b7
001022258 773__ $$a10.48550/arXiv.2311.06074$$tarXiv$$y2023
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001022258 9141_ $$y2024
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