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CATEGORIES:Inference Group
SUMMARY:Bayesian estimation of neuronal connectivity from
MEA recordings - Fabio Rigat
DTSTART;TZID=Europe/London:20061122T140000
DTEND;TZID=Europe/London:20061122T150000
UID:TALK5717AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/5717
DESCRIPTION:Multi-electrode arrays (MEAs) provide simulaten
eous extra-cellular recordings of the spiking
activity of several neurons cultured in-vitro
over long periods of time. However\, these data
are typically\ncontaminated by various sources of
noise such as spike sorting errors. The availabili
ty of these noisy recordings provides the oppor
tunity for developing statistical models of the n
eurons' firing patterns to\nestimate their functi
onal characteristics and to predict their spiking
behaviour as a function external stimulation.\n\nI
n this seminar I will present a novel hierarchic
al dynamic Bayesian network model describing the
spiking patterns of neuronal ensembles over time
. The parameters characterizing the discrete-ti
me spiking\nprocess\, the unknown structure of the
functional connections among the analysed neurons
and its dependence on their spatial arrangement
are introduced at separate model stages. Po
sterior estimates for all model parameters and
predictions for future spiking states are com
puted via the Gibbs sampler using a shrinkage pr
ior. The adequacy of the model is investigate
d by plotting the\nraw residuals and by applying
the time-rescaling theorem. I will also illustrat
e the analysis of a set of experimental MEA
recordings showing that one neuron has a pivot
al role for the initiation and persistence of t
he network activity and that the estimated netw
ork structure significantly depends on the spat
ial arrangement of the\nneurons.\n\nThis is jo
int work with Mathisca de Gunst (Vrije Unive
rsiteit\, Amsterdam) and with Jaap van Pelt (Ne
therlands Institute for Brain Research\, Amsterda
m).
LOCATION:Ryle Seminar Room\, Cavendish Laboratory
CONTACT:Christian Steinruecken
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