Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
BP: Fachverband Biologische Physik
BP 13: Signaling, Biological Networks
BP 13.4: Vortrag
Mittwoch, 29. März 2023, 10:30–10:45, BAR 0106
Towards statistical models of activity recordings from stem cell derived neuronal networks — •Sebastian Willenberg1, Elijah R. Shelton1, Paulina M. Wysmolek2, Filippo D. Kiessler1, Achim Brinkop1, and Friedhelm Serwane1,2,3 — 1Faculty of Physics and CeNS, LMU, Munich, Germany — 2MPI for Medical Research, Heidelberg, Germany — 3Munich Cluster for Systems Neurology, Munich, Germany
Analysis of neuronal activity is the key to understanding the principles of brain circuitry. Theoretical models have been applied on many different scales, ranging from the analysis of single neuron activity to the collective behaviour of large groups of neurons. Models from statistical physics describe the behaviour of networks across spatial and temporal scales with a minimal amount of parameters. Until now, those models have mainly been applied to datasets recorded via 2D electrode arrays. This makes accessing 3D network morphology challenging. I will present our first steps applying statistical models to neuronal recordings of stem cell derived neuronal networks obtained using lightsheet1 or confocal microscopy. To model the collective firing we map single neuron activity to two states and apply a maximum entropy model to calculate the entropy and energy following the approach of Tkačik et al.2. Using this approach, we seek a minimal model describing the firing activity which allows us to understand and predict the collective behaviour of in vitro neuronal networks.
1: Wysmolek et al., Sci Rep 12, 20420, 2022
2: Tkačik et al., PNAS 112, 11508, 2015