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UID:20251121T125333EST-5724FhLv1e@132.216.98.100
DTSTAMP:20251121T175333Z
DESCRIPTION:Title: Averaging principles for Markovian models of synaptic pl
 asticity\n\nAbstract: In neuroscience\, synaptic plasticity refers to the 
 set of mechanisms driving the dynamics of neuronal connections\, called sy
 napses and represented by a scalar value\, the synaptic weight. In this ta
 lk I will consider a stochastic system with two connected neurons\, with a
  variable synaptic weight that depends on point processes associated to ea
 ch neuron. The input neuron is represented by an homogeneous Poisson proce
 ss\, whereas the output neuron jumps with an intensity that depends on the
  jumps of the input node and the connection intensity. I will study a scal
 ing regime where the rate of both point processes is large compared to the
  dynamics of the connection\, that corresponds to a classical assumption i
 n computational neuroscience where cellular processes evolve much more rap
 idly than the synaptic weight. I will present an averaging principle for t
 he time evolution of the connection intensity\, and a sketch of its proof\
 , which involves a detailed analysis of several of unbounded additive func
 tionals in the slow-fast limit\, and technical results on interacting shot
 -noise processes.\n\nTo attend Zoom meeting please contact elliot.paquette
 ! [at] mcgill.ca\n\n \n
DTSTART:20220210T163000Z
DTEND:20220210T173000Z
LOCATION:Room 708\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue 
 Sherbrooke Ouest
SUMMARY:Gaëtan Vignoud
URL:/mathstat/channels/event/gaetan-vignoud-337395
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