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{ "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": "2018-01-01T13:00:00-05:00", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/27894/galley/17532/download/" } ] }