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BEGIN:VEVENT
DTSTAMP:20191014T080040Z
LAST-MODIFIED:20190911T174123Z
DTSTART:20190923T140000Z
DTEND:20190923T150000Z
UID:event2183@bu.edu
URL:http://physics.bu.edu/events/show/2183
SUMMARY:Scale-Invariant Neural Dynamics for Cognition
DESCRIPTION:Featuring Yue Liu\n\nPart of the Preliminary Oral Exam.\n\nExam
ining Committee: Marc W. Howard\, Michael E.Hasselmo\, Pankaj Mehta\, Marti
n Schmaltz\n\n\nAbstract: The brain is operating in a world with rich dyna
mics across a wide range of timescales\, therefore brain state should refle
ct this dynamics. Limited by experimental techniques and the nature of beha
vior\, most established results in systems neuroscience are about static fe
ature detectors. New techniques for large-scale and chronic measurement of
neural activity open up the opportunity to investigate neural dynamics acro
ss different timescales. In this talk I will present modeling\, theoretical
and data analysis works on a particular type of temporal dynamics - scale-
invariant dynamics - which has been implicated by behavioral experiments an
d neural data. I will start with a neural circuit model that utilizes Lapla
ce transform and inverse Laplace transform to produce scale-invariant seque
ntial neural activity and point out evidence for the elements of model in n
eural data. I will then present a theoretical analysis on the ability for
a linear recurrent neural network to generate scale-invariant neural activi
ty. I will show that a geometric series of eigenvalues and translated eigen
vectors in the connectivity matrix are needed for generating scale-invarian
t activity. Finally I will show the existence of reliable neural dynamics o
n the timescale of minutes in neural data. Taken together\, these results s
uggest the possibility that the neural activity has important dynamics over
a much wider range of timescales then previously thought\, and explore its
consequences for neural circuit models.\n_________________________________
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LOCATION:CILSE 106C\, 610 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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