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METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20261019T024259Z
LAST-MODIFIED:20200904T200417Z
DTSTART:20200914T180000Z
DTEND:20200914T190000Z
UID:event2353@bu.edu
URL:http://physics.bu.edu/internal/events/show/2353
SUMMARY:NEURONAL DYNAMICS ACROSS MACROSCOPIC TIMESCALES
DESCRIPTION:Featuring Yue Liu\, Boston University\, Physics Department\n\nP
	art of the PhD Final Oral Exams.\n\nThe brain operates in a world with rich
	 dynamics across a wide range of timescales\, including those on the order 
	of seconds and above. Behavioral experiments on memory and timing reveal st
	riking similarities in the behavioral patterns across a vast range of times
	cales from seconds to minutes. To subserve these behavioral patterns and ad
	apt to natural statistics\, the collective activity of the large of number 
	of neurons in the brain should exhibit similar dynamics over a spectrum of 
	macroscopic timescales\, an idea we refer to as scale-invariance. Recently\
	, new techniques for large-scale and chronic measurements of neural activit
	y open up the opportunity to investigate neuronal dynamics across different
	 macroscopic timescales. This talk presents modeling\, theoretical and empi
	rical work that center around the idea of scale-invariance. After a brief i
	ntroduction on the empirical evidence for scale-invariance\, I will start w
	ith a biophysically-realistic neural circuit model that combines a detailed
	 simulation of a calcium-activated membrane current with the mathematical f
	ormalism of the inverse Laplace transform to produce sequential neural acti
	vity with the scale-invariant property. Next\, I will present a theoretical
	 analysis on the ability of linear recurrent neural networks to generate sc
	ale-invariant neural activity. It is shown that the network connectivity ma
	trix should have a geometric series of eigenvalues and translated eigenvect
	ors if the eigenvalues are real and distinct. Lastly\, I will show an empir
	ical analysis of neural data motivated by scale-invariance. The analysis re
	veals the existence of repeatable neuronal dynamics on the timescale of bot
	h seconds and minutes in multiple neural recordings of rodents performing c
	ognitive tasks.\n\n------\n\nZoom information\nhttps://bostonu.zoom.us/j/93
	271109794?pwd=MXIrNVpSbVlGai9WQkpUbm0yK1grZz09Meeting ID: 932 7110 9794\nPa
	sscode: 550550
LOCATION: \, \, 
STATUS:CONFIRMED
CLASS:PUBLIC
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