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SUMMARY:Dual missions of neural dimensionality reduction
DTSTART:20230602T140000
DTEND:20230602T160000
DTSTAMP:20260928T184410Z
UID:e4af60403dad507b5bfec5cab9f56efe2fa9f72bd193e2b7cd71a934
CATEGORIES:Conferences - Seminars
DESCRIPTION:Shuqi Wang\nEDIC candidacy exam\nExam president: Prof. Michael
  Gastpar\nThesis advisor: Prof. Wulfram Gerstner\nCo-examiner: Prof. Macke
 nzie Mathis\n\nAbstract\nOver the last decades\, as more neurons are being
  simultaneously recorded\, researchers embrace the new opportunity of anal
 yzing how neural circuits coordinate as a whole to drive behavior. Well su
 ited to this mission\, various dimensionality reduction methods have been 
 developed and many have demonstrated the ability to find latent variables 
 that correlate with behavior. In this report\, I will review three represe
 ntative methods and highlight their key methodologies. Furthermore\, in ad
 dition to building the link to behavior\, I will argue that there is anoth
 er equally important but far less explored mission of neural dimensionalit
 y reduction\, which is to provide insights into the connectivity structure
 .\n\nBackground papers\n\n	Pandarinath\, Chethan\, et al. "Inferring singl
 e-trial neural population dynamics using sequential auto-encoders." Natur
 e methods 15.10 (2018): 805-815. https://www.nature.com/articles/s41592-
 018-0109-9\n	Schneider\, Steffen\, Jin Hwa Lee\, and Mackenzie Weygandt Ma
 this. "Learnable latent embeddings for joint behavioral and neural analysi
 s." arXiv preprint arXiv:2204.00673 (2022). https://arxiv.org/abs/2204.
 00673\n	Yu\, Byron M.\, et al. "Gaussian-process factor analysis for low-d
 imensional single-trial analysis of neural population activity." Advances
  in neural information processing systems 21 (2008). https://proceedings
 .neurips.cc/paper/2008/hash/ad972f10e0800b49d76fed33a21f6698-Abstract.html
 \n
LOCATION:SV 2510 https://plan.epfl.ch/?room==SV%202510
STATUS:CONFIRMED
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