BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Accurate and fast simulation of channel noise in conductance-based
  model neurons by diffusion approximation
DTSTART:20101214T151500
DTSTAMP:20260427T200405Z
UID:d4b06b8d652853fb86cb694b79d43d38e7a6b56a83208e65be8e5a7b
CATEGORIES:Conferences - Seminars
DESCRIPTION:Daniele LINARO\nStochastic channel gating is the major source 
 of intrinsic neuronal noise\, whose functional consequences at the microci
 rcuit- and network-levels have been only partly explored. A systematic stu
 dy of this channel noise in large ensembles of biophysically detailed mode
 l neurons calls for the availability of fast numerical methods. In fact\, 
 exact techniques employ the microscopic simulation of the random opening a
 nd closing of individual ion channels\, usually based on Markov models\, w
 hose computational loads are prohibitive for next generation massive compu
 ter models of the brain. In this work\, we operatively define a procedure 
 for translating any Markov model describing voltage- or ligand-gated membr
 ane ion-conductances into an effective stochastic version\, whose computer
  simulation is efficient\, without compromising accuracy. Our approximatio
 n is based on an improved Langevin-like approach\, which employs stochasti
 c differential equations and no Montecarlo methods. As opposed to an earli
 er proposal recently debated in the literature\, our approximation reprodu
 ces accurately the statistical properties of the exact microscopic simulat
 ions\, under a variety of conditions\, from spontaneous to evoked response
  features. In addition\, our method is not restricted to the Hodgkin-Huxle
 y sodium and potassium currents and is general for a variety of voltage- a
 nd ligand-gated ion currents 
LOCATION:CO 016
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR