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VERSION:2.0
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METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20260814T020448Z
LAST-MODIFIED:20190911T174123Z
DTSTART:20190923T140000Z
DTEND:20190923T150000Z
UID:event2183@bu.edu
URL:http://physics.bu.edu/internal/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_________________________________
	_______
LOCATION:RKC 106C\, 610 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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