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SUMMARY:Quantifying individuality in neural circuit representations - Alex
  Williams (NYU)
DTSTART:20250513T130000Z
DTEND:20250513T143000Z
UID:TALK232024@talks.cam.ac.uk
CONTACT:Daniel Kornai
DESCRIPTION:Signatures of neural computation are thought to be reflected i
 n the coordinated activity of large neural populations. Neuroscience is no
 w flush with measurements of these activity patterns in humans\, animal su
 bjects\, and large-scale artificial network models. In this talk\, I will 
 address an extensively studied\, yet unresolved\, question: How should we 
 quantify the extent to which two or more neural circuits have “similar
 ” activation patterns? Without an answer to this question\, the field ha
 s struggled to investigate basic questions about biological variability an
 d individuality\, such as: How do neural representations vary across a hea
 lthy population? How do differences in neural population activity correlat
 e with behavioral idiosyncrasies and disorders? How similar are computatio
 nal mechanisms in biological brains and artificial neural networks? In thi
 s talk\, I will summarize several mathematical methods that quantify simil
 arity in neural representations and demonstrate how they provide early ins
 ights into these questions when applied to biological data and artificial 
 networks.
LOCATION:CBL Seminar Room\, Engineering Department\, 4th floor Baker build
 ing
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