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SUMMARY:CBL Alumni Talk: Nonlinear filtering as a unifying principle in ne
 uroscience by Jean-Pascal Pfister - Jean-Pascal Pfister\, University of Zu
 rich and ETH Zurich
DTSTART:20210702T150000Z
DTEND:20210702T160000Z
UID:TALK160552@talks.cam.ac.uk
CONTACT:Elre Oldewage
DESCRIPTION:A remarkable property of the brain is its ability to perform r
 obust computation while being made of unreliable elements. For example\, s
 ynapses are highly stochastic elements that often fail to transmit the inf
 ormation across the synaptic cleft. Similarly\, at the neuronal level\, th
 e action potential generation is best described by a stochastic process an
 d is therefore not fully deterministic. It remains therefore unclear how t
 he brain performs reliable computation with unreliable components. In this
  talk\, I will argue that a fundamental task that the brain needs to solve
  is the dynamical extraction of relevant information from a continuous str
 eam of unreliable observations. This task can be generically formulated as
  a nonlinear Bayesian filtering task. I will therefore reinterpret several
  phenomena in neuroscience from this nonlinear filtering principle. Short-
 term plasticity will be seen as a nonlinear filter that estimates the pres
 ynaptic membrane potential from observed spikes. Long-term plasticity will
  be seen as a nonlinear filter that estimates the dynamically changing gro
 und truth weights. Finally neuronal dynamics will be seen as a nonlinear f
 ilter that dynamically extracts features from synaptic inputs.
LOCATION:https://eng-cam.zoom.us/j/84626937837?pwd=RC95RTdybkRlNHhOcThjclQ
 zYUxkdz09
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