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SUMMARY: Simplicial complexes for analysis of neural data
DTSTART:20151009T140000
DTEND:20151009T150000
DTSTAMP:20260411T101743Z
UID:875ae7068812cabc1759dbf75e4d60abf8a58b038a9631a57a76bfe9
CATEGORIES:Conferences - Seminars
DESCRIPTION:Chad Giusti (Warren Center for Network and Data Sciences\, Uni
 versity of Pennsylvania)\nGraphs have proven to be an exceptional data str
 ucture through which to address a broad range problems in neuroscience. Ho
 wever\, they are intrinsically limited to the study of dyadic relationship
 s\, as represented by an edge (or its absence) between two population elem
 ents. In the brain\, it is often clear that fundamental functional units o
 f interest involve large groups of basic units\, which suggests that graph
  models are insufficient for their study. Simplicial complexes offer a nat
 ural way to address this concern\, with the added benefit of providing a b
 ridge for the application of powerful topological tools. A central difficu
 lty in using simplicial methods\, however\, is the construction from obser
 vations of complexes whose topological or combinatorial structure tells us
  something useful about the underlying neural system. Here\, we discuss a 
 pair of complexes\, the order complex and the coincidence complex\, that h
 ave proven effective for understanding neural data across various modaliti
 es\, along with a discussion of how to measure interesting structure.
LOCATION:Campus Biotech\, Building B1\, 6th floor
STATUS:CONFIRMED
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