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Non-Parametric Bayesian Inference for Change Point Detection in Neural Spike Trains

Alt, Bastian ; Messer, Michael ; Roeper, Jochen ; Schneider, Gaby ; Koeppl, Heinz :
Non-Parametric Bayesian Inference for Change Point Detection in Neural Spike Trains.
In: 2018 IEEE Statistical Signal Processing Workshop (SSP) (SSP 2018), Freiburg im Breisgau, Germany. Freiburg im Breisgau, Germany
[Konferenz- oder Workshop-Beitrag], (2018)

Kurzbeschreibung (Abstract)

We present a model for point processes with gamma distributed increments. We assume a piecewise constant latent process controlling shape and scale of the distribution. For the discrete number of states of the latent process we use a non-parametric assumption by utilizing a Chinese restaurant process (CRP). For the inference of such inhomogeneous gamma processes with an unbounded number of states we do Bayesian inference using Markov Chain Monte Carlo. Finally, we apply the inference algorithm to simulated point processes and to empirical spike train recordings, which inherently possess non-stationary and non-Poissonian behavior.

Typ des Eintrags: Konferenz- oder Workshop-Beitrag (Keine Angabe)
Erschienen: 2018
Autor(en): Alt, Bastian ; Messer, Michael ; Roeper, Jochen ; Schneider, Gaby ; Koeppl, Heinz
Titel: Non-Parametric Bayesian Inference for Change Point Detection in Neural Spike Trains
Sprache: Englisch
Kurzbeschreibung (Abstract):

We present a model for point processes with gamma distributed increments. We assume a piecewise constant latent process controlling shape and scale of the distribution. For the discrete number of states of the latent process we use a non-parametric assumption by utilizing a Chinese restaurant process (CRP). For the inference of such inhomogeneous gamma processes with an unbounded number of states we do Bayesian inference using Markov Chain Monte Carlo. Finally, we apply the inference algorithm to simulated point processes and to empirical spike train recordings, which inherently possess non-stationary and non-Poissonian behavior.

Ort: Freiburg im Breisgau, Germany
Freie Schlagworte: Inhomogeneous Gamma Process; Bayesian Non-Parametrics; Neural Spike Trains; Change Points
Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > B: Adaptionsmechanismen > Teilprojekt B4: Planung
Veranstaltungstitel: 2018 IEEE Statistical Signal Processing Workshop (SSP) (SSP 2018)
Veranstaltungsort: Freiburg im Breisgau, Germany
Hinterlegungsdatum: 26 Apr 2018 22:35
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