{"pk":27894,"title":"Supervised Learning of Actino Selection in Cognitive Spiking Neoron Models","subtitle":null,"abstract":"We have previously shown that a biologically realistic spikingneuron implementation of an action selection/executionsystem (constrained by the neurological connectivity of thecortex, basal ganglia, and thalamus) is capable of performingcomplex tasks, such as the Tower of Hanoi, n-Back, andsemantic memory search. However, because the neuralimplementation approximates a strict rule-based structure of aproduction system, such models have involved hand-tweakingof multiple parameters to get the desired behaviour. Here, weshow that a simple, local, online learning rule can be used tolearn these parameters, resulting in neural models of cognitivebehaviours that are more reliable and easier to construct thanwith prior methods.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"neural engineering framework"},{"word":"neural production systems"},{"word":"semantic pointer architecture"},{"word":"spiking neurons"},{"word":"basal ganglia"},{"word":"neural cognitive architectures"}],"section":"Publication-based-Talks","is_remote":true,"remote_url":"https://escholarship.org/uc/item/4wf00586","frozenauthors":[{"first_name":"Terrence","middle_name":"C","last_name":"Stewart","name_suffix":"","institution":"UWaterloo","department":""},{"first_name":"Sverrir","middle_name":"","last_name":"Thorgeirsson","name_suffix":"","institution":"UWaterloo","department":""},{"first_name":"Chris","middle_name":"","last_name":"Eliasmith","name_suffix":"","institution":"UWaterloo","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2018-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/27894/galley/17532/download/"}]}