Neuronal dynamics is the result of a multitude of biological processes that coexist and interplay at different time scales. A big challenge in neuroscience is to understand which elements of this complex dynamics encode information and, ultimately, generate behaviour. This requires finding structure in such variability. In this regard, unsupervised data mining constitutes a powerful opportunity. In this talk, I will propose different unsupervised approaches to investigate neuronal time series to uncover and model hidden regularities in experimental data. From cell assembly detection to dynamical modelling, I will exemplify this approach by discussing results from the investigation of reinforcement learning in striatal and prefrontal regions, hippocampal phase coding and spontaneous behaviour coding in the sensorimotor cortex of rodents.


Eleonora Russo is an independent research fellow at the Johannes Gutenberg University of Mainz (Germany). She studied physics at the University of Pisa in Italy and received a PhD in Neuroscience from the International School for Advanced Studies (SISSA) in Trieste for her work on modelling free association dynamics in the cortex. She moved to Mannheim (Germany) as postdoctoral fellow at the Central Institute of Mental Health, where later became an Independent Research Fellow of the Chica and Heinz Foundation. Her research focuses on the investigation of cognitive processes by combining modelling with mining experimental data with statistical machine learning techniques.


This is an hybrid seminar.

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