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DTSTART:20170312T070000
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DTSTART:20161106T060000
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DTSTART;TZID=America/Toronto:20170503T150000
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URL:https://uwaterloo.ca/artificial-intelligence-group/events/phd-seminar-r
 epresenting-time-recurrent-spiking-neural
LOCATION:DC - William G. Davis Computer Research Centre 200 University Aven
 ue West 2310 Waterloo ON N2L 3G1 Canada
SUMMARY:PhD Seminar: Representing Time in Recurrent Spiking Neural Networks
CLASS:PUBLIC
DESCRIPTION:Speaker: Aaron R. Voelker\, PhD Candidate\n\nOne of the central
  challenges in neuroscience involves understanding\nhow dynamic stimuli ca
 n be processed by neural mechanisms to drive\nbehavior. By synthesizing mo
 dels from neuroscience with tools from\nsignal processing and control theo
 ry\, we derive the feedback weights\nrequired to represent a low-frequency
  input signal by delaying it\nalong a low-dimensional manifold\, using a r
 ecurrently connected\nnetwork of biologically plausible spiking neurons. T
 his is shown to\nnonlinearly encode the continuous-time history of an inpu
 t signal by\noptimally compressing it into a low-dimensional subspace.
DTSTAMP:20260324T064651Z
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