Our project focuses on developing machine learning and statistical approached to anlayze brain activity at multiple scales.

Biological neural networks are characterized by strong recurrence, and widespread, bidirectional connectivity at multiple scales. From this structure emerges a complex dynamics where elements of this network cooperate at different levels to give rise, for example, to coherent behaviors and percepts. This complexity manifests itself as collective oscillations as well as more complex dymamical patterns such as Sharp-wave ripple complexes, that can be observed in electrical brain activity. These neural events are believed to play a key role in information processing, learning and behavior. Our research aims at designing better techniques to detect these events and understand their underlying mechanisms and computational role.