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UID:20260412T233736EDT-4624JXx2Z7@132.216.98.100
DTSTAMP:20260413T033736Z
DESCRIPTION:\n	BRaIN Seminar Series\n\n	Brain Repair and Integrative Neurosci
 ence Program The BRaIN Program of the RI-MUHC is pleased to announce our n
 ext presentation under the theme:Vision and Cognitive Neuroscience\n\n	Spea
 ker: Dr. Steven Zucker\n\n	David & Lucile Packard Professor of Computer Sci
 ence & Biomedical Engineering\n\n	Yale University\, School of Engineering &
  Applied Science\n\n	Founding member of the Centre for Intelligent Machines
  (CIM)\n\n\n \n\nAbstract\n\n \n\n\n	How might one infer circuit properties
  from neurophysiological data? How do these circuits relate to artificial 
 neural networks?\n\n		 \n\n		We address these challenges with a novel neural m
 anifold. It is obtained using unsupervised machine learning algorithms and
  applied to the mouse visual system. Each point on our manifold is a neuro
 n\; nearby neurons respond similarly in time to similar parts of a stimulu
 s ensemble. This ensemble includes drifting gratings and flows\, i.e. patt
 erns resembling what a mouse would 'see' while running through fields. Our
  manifold differs from the standard practice in computational neuroscience
 \, of embedding trials in neural coordinates. Importantly\, for our manifo
 lds topology matters: from spectral theory we infer that\, if the circuit 
 consists of separate components\, the manifold is discontinuous (illustrat
 ed with retinal data). If there is significant overlap between circuits\, 
 the manifold is nearly-continuous (cortical data).  To approach real circu
 its\, local neighborhoods on the manifold are identified with actual circu
 it components. For the retinal data we show these components correspond to
  distinct ganglion cell types by their mosaic-like receptive field organiz
 ation\, while for cortical data\, neighborhoods organize neurons by type (
 excitatory/inhibitory) and anatomical layer. The manifold topology for dee
 p CNN's will also be developed.\n			\n			Joint research with Luciano Dyballa (Ya
 le)\, Marija Rudzite (Duke)\, Michael Styrker (UCSF) and Greg Field (UCLA)
 .\n	\n\n
DTSTART:20230720T180000Z
DTEND:20230720T190000Z
LOCATION:CA\, MGH L7 140
SUMMARY:Toward a manifold encoding neural responses in the visual system
URL:https://www.mcgill.ca/cim/channels/event/toward-manifold-encoding-neura
 l-responses-visual-system-351713
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