Approaches for analyzing massively parallel neuronal data – Current and future

Organizers

Sonja Grün | Juelich Research Centre, Germany
Jonas Oberste-Frielinghaus | Juelich Research Centre, Germany

Abstract

This session will present different approaches to analyze massively parallel neuronal data on the single neuron level in behaving animals during complex behavior. Several decades of research have clearly demonstrated that neurons do not act in isolation but achieve dynamic behavioral control in large networks that depend on neuronal interaction. To detect this interaction in living brains, large numbers of neurons must be recorded simultaneously to understand, e.g. how sensory-motor loops and behavioral control are organized at the level of populations of neurons.

Schedule (CEST)

Wednesday, Sept 27

08:30

Fabian Sinz | University of Göttingen, Germany
Exploring the Visual System with Functional Digital Twins and Inception Loops

09:00

Stefan Rotter | University of Freiburg, Germany
Inferring large networks from massively parallel neuronal signals

09:30

Evren Gokcen | Carnegie Mellon University, USA
Disentangling the flow of signals between populations of neurons

10:00

30 min coffee break

10:30

Alex Williams | New York University, USA
Signal in the noise: Why trial-to-trial and animal-to-animal variability matters for systems neuroscience

11:00

Jonas Oberste-Frielinghaus | Juelich Research Centre, Germany
Population unitary event analysis in experimental data and network simulations

11:30

Alessandra Stella | University of Turin, Italy
Signatures of robust cell assemblies in parallel spike trains

12:00

Discussion

12:30

End