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SUMMARY:Finding needles in the neural haystack: unsupervised analyses of n
 oisy data - Marine Schimel\, Kris Jensen\, Department of Engineering
DTSTART:20211201T170000Z
DTEND:20211201T180000Z
UID:TALK166495@talks.cam.ac.uk
CONTACT:Katharina Zuhlsdorff
DESCRIPTION:In modern neuroscience\, we often want to extract information 
 from recordings of many neurons in the brain. Unfortunately\, the activity
  of individual neurons is very noisy\, making it difficult to relate to co
 gnition and behavior. Thankfully\, we can use the correlations across time
  and neurons to denoise the data we record. In particular\, using recent a
 dvances in machine learning\, we can build models which harness this struc
 ture in the data to extract more interpretable signals. In this talk\, we 
 present two such methods as well as examples of how they can help us gain 
 further insights into the neural underpinnings of behavior.
LOCATION:Zoom
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