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SUMMARY:Hierarchical Bayesian inference in networks of spiking neurons - P
 hilip Sterne (University of Cambridge)
DTSTART:20070924T100000Z
DTEND:20070924T110000Z
UID:TALK8045@talks.cam.ac.uk
CONTACT:Philip Sterne
DESCRIPTION:This week we will be looking at:\nRao (2005) - Hierarchical Ba
 yesian inference in networks of spiking neurons\nAvailable from:\nhttp://w
 ww.cs.washington.edu/homes/rao/rao_nips04.pdf\n\nAbstract:\n\nThere is gro
 wing evidence from psychophysical and neurophysiological studies that the 
 brain utilizes Bayesian principles for inference and decision making. An i
 mportant open question is how Bayesian inference for arbitrary graphical m
 odels can be implemented in networks of spiking neurons. In this paper\, w
 e show that recurrent networks of noisy integrate-and-fire neurons can per
 form approximate Bayesian inference for dynamic and hierarchical graphical
  models.\n
LOCATION:TCM Seminar Room\, Cavendish Laboratory\, Department of Physics
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