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VERSION:2.0
PRODID:-//RLASKEY//CALENDEROUS//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20260730T173653Z
LAST-MODIFIED:20121116T203436Z
DTSTART:20111007T160000Z
DTEND:20111007T170000Z
UID:event830@bu.edu
URL:http://physics.bu.edu/internal/events/show/830
SUMMARY:Models of the insect brain for odor discrimination and decision mak
	ing
DESCRIPTION:Featuring Ramon Huerta\, BioCircuits Institute\, UCSD\nHosted b
	y: Plamen Ivanov\n\nPart of the Biophysics/Condensed Matter Seminar Series.
	\n\nAbstract:\n \n In the course of evolution animals\, bacteria and plants
	 have developed\n sophisticated methods and algorithms for solving difficul
	t problems in\n chemical sensing very efficiently. Complex signalling pathw
	ays inside\n single cells can trigger movement toward the source of a nutri
	ent.\n Complex networks of neurons are able to compute odor types and the\n
	 distance to a source in turbulent flows.  These networks of neurons u
	se\n a combination of temporal coding\, layered structures\, simple Hebbian
	\n learning rules\, reinforcement learning and inhibition to quickly\n lear
	n about chemical stimuli that are critical for their survival.\n \n In this
	 talk we revisit the critical elements of the insect brain involved\n in od
	or discrimination and determine the impact that each of the areas have\n in
	 learning an odor discrimination task. We apply these lessons to the\n prob
	lem of gas identification with artificial sensor arrays.\n The insect brain
	 must cope with those conditions by preprocessing\n the data using the exci
	tatory-inhibitory network in the first\n relay station of the insect olfact
	ory system.It manages to\n extract and dynamically inhibit common odor repr
	esentation and\n enhances the sensitivity to novel ones. Thus\, we use the 
	insect\n olfactory system as a base and inspiration to build odor recogniti
	on\n devices that take advantage of the spatio-temporal characteristics of\
	n the turbulent gas plume. We demostrate how they can be merged and\n compa
	red to state-of-the-art machine learning algorithms.
LOCATION:SCI 352\, 590 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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