BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Talks.cam//talks.cam.ac.uk// X-WR-CALNAME:Talks.cam BEGIN:VEVENT SUMMARY:Bayesian estimation of neuronal connectivity from MEA recordings - Fabio Rigat DTSTART:20061122T140000Z DTEND:20061122T150000Z UID:TALK5717@talks.cam.ac.uk CONTACT:Christian Steinruecken DESCRIPTION:Multi-electrode arrays (MEAs) provide simulateneous extra- cellular recordings of the spiking activity of several neurons cul tured in-vitro over long periods of time. However\, these data are typica lly\ncontaminated by various sources of noise such as spike sorting errors . The availability of these noisy recordings provides the opportunity f or developing statistical models of the neurons' firing patterns to\nest imate their functional characteristics and to predict their spiking behavi our as a function external stimulation.\n\nIn this seminar I will present a novel hierarchical dynamic Bayesian network model describing the spik ing patterns of neuronal ensembles over time. The parameters character izing the discrete-time spiking\nprocess\, the unknown structure of the f unctional connections among the analysed neurons and its dependence on th eir spatial arrangement are introduced at separate model stages. Po sterior estimates for all model parameters and predictions for futur e spiking states are computed via the Gibbs sampler using a shrinkage prior. The adequacy of the model is investigated by plotting the\nra w residuals and by applying the time-rescaling theorem. I will also illus trate the analysis of a set of experimental MEA recordings showing that one neuron has a pivotal role for the initiation and persistence of the network activity and that the estimated network structure sig nificantly depends on the spatial arrangement of the\nneurons.\n\nThis is joint work with Mathisca de Gunst (Vrije Universiteit\, Amster dam) and with Jaap van Pelt (Netherlands Institute for Brain Research\ , Amsterdam). LOCATION:Ryle Seminar Room\, Cavendish Laboratory END:VEVENT END:VCALENDAR