BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4//
BEGIN:VEVENT
UID:20250628T041522EDT-1602ovxF43@132.216.98.100
DTSTAMP:20250628T081522Z
DESCRIPTION:Making sense of large-scale neural and behavioral data\n\nCarse
 n Stringer\, Howard Hughes Medical Institute\n	Tuesday October 3\, 12-1pm\n
 	Zoom Link: https://mcgill.zoom.us/j/86855481591\n	In Person: 550 Sherbrooke
 \, Room 189\n	\n	Abstract: Advances in protein engineering and microscopy ha
 ve enabled routine recordings of over 50\,000 neurons simultaneously in th
 e mouse cortex at a sampling rate of 3Hz\, or ~8\,000 neurons at a rate of
  30Hz. What properties does this large-scale activity have? One popular hy
 pothesis is that this neural activity is “simple” and low-dimensional\, an
 d we can summarize even 50\,000 neuron recordings with just a few numbers 
 at any one time. Many analytical tools and theories have been developed ba
 sed on this assumption. However\, in our large-scale recordings we found t
 hat neural responses to visual stimuli were high-dimensional\, exploring m
 any diverse patterns of activity that could not be reduced to a few number
 s. This high-dimensional structure that cannot be easily captured by exist
 ing data visualization methods. We therefore developed an embedding algori
 thm called Rastermap\, which captures complex temporal and highly nonlinea
 r relationships between neurons\, and provides useful visualizations by as
 signing each neuron to a location in the embedding space. We found within 
 neural datasets from virtual reality tasks unique subpopulations of neuron
 s encoding abstract elements of decision-making\, the environment and beha
 vioral states. Further\, we found that ongoing “spontaneous” activity in c
 ortex was high-dimensional\, representing the moment-to-moment behaviors o
 f the mouse. To interrogate behavioral representations in the mouse brain\
 , we developed the fast Facemap network for tracking 13 distinct points on
  the mouse face recorded from arbitrary camera angles. We used Facemap to 
 find that the neuronal activity clusters which were highly driven by behav
 iors were more spatially spread-out across cortex. We also found that the 
 deep keypoint features inferred by the model had time-asymmetrical state d
 ynamics that were not apparent in the raw keypoint data. In summary\, Face
 map provides a stepping stone towards understanding the function of the br
 ainwide neural signals and their relation to behavior.\n
DTSTART:20231003T160000Z
DTEND:20231003T170000Z
SUMMARY:QLS Seminar Series - Carsen Stringer
URL:/qls/channels/event/qls-seminar-series-carsen-stri
 nger-350907
END:VEVENT
END:VCALENDAR