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SUMMARY:Using persistent homology to reveal hidden information in neural d
 ata
DTSTART:20151016T140000
DTEND:20151016T150000
DTSTAMP:20260511T041821Z
UID:5f3bb83ddb451f836e589b1ad9f1b05138e5ec5ac9539c302a72533f
CATEGORIES:Conferences - Seminars
DESCRIPTION:Gard Spreeman (NTNU)\n\n	 \nMammalian navigation is aided by 
 place cells\, which are neurons that\nfire preferentially when the animal 
 is in certain regions of space. It\nis known that the firing activities of
  these and related neurons are\nnot governed solely by spatial position\, 
 but also by head direction\,\ntheta wave phase\, sensory stimuli\, etc.\, 
 and probably also by further\nunknown influences. Such covariates are thou
 ght of as being reflected\nin the animal's "state space"\, and knowledge o
 f its topological\nproperties can reveal hidden information about a priori
  unknown\ncovariates.\nWe propose a method wherein an approximation of suc
 h a state space is\nbuilt from spike train recordings of neurons. Persiste
 nt homology is\nthen used to reveal properties of the space. Through an in
 ference\nprocess\, we remove the contributions of known covariates to the 
 spike\ntrains\, and thus to the reconstructed stace space. After all known
 \ncovariates have been accounted for\, persistent homology reveals\nproper
 ties of any potential remaining unknown ones.\n\n	 
LOCATION:Campus Biotech\, Building B1\, 6th floor
STATUS:CONFIRMED
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