{"pk":31353,"title":"Self-Organization of Auditory Motion Detectors","subtitle":null,"abstract":"This work addresses the question of how neural networks self-organise to recognize familiar sequential patterns. A neural network model with mild constraints on its initial architecture learns to encode the direction of spectral motion as auditory stimuli excite the units in a tonotopically arranged input layer like that found after peripheral processing by the cochlea. The network consists of a series of inhibitory clusters with excitatory interconnections that self-organize as streams of stimuli excite the clusters over time. Self-organization is achieved by application of the learning heuristics developed by Marshall (1990^ for the self-organization of excitatory and inhibitory pathways in visual motion detection. These heuristics are implemented through linear thresholding equations for unit activation having faster-than-linear inhibitory response. Synaptic weights are learned throughout processing according to the competitive algorithm explored in Malsburg (1973).","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Posters","is_remote":true,"remote_url":"https://escholarship.org/uc/item/1xg3m4s8","frozenauthors":[{"first_name":"Sven","middle_name":"E.","last_name":"Anderson","name_suffix":"","institution":"Indiana University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"1992-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/31353/galley/22422/download/"}]}