Dynamic Data meets Neuronal Networks

Neuronal networks are remarkable in their ability to perform a diversity of dynamic tasks such as sensory processing, information transfer and storage. Unraveling how information travels through these networks over time, and how it is being processed, will enable classification of functionality, network wiring, and synthesize future dynamics. Typically, such networks are extremely challenging to study using traditional approaches since on top of their complex structure they exhibit intricate time-dependent dynamics. My talk will focus on our recently developed methods that leverage sampled time-series data from a network. I will describe how to fuse dynamical system theory with data analysis (e.g. phase space analysis, model reduction, optimization and probabilistic graphical modeling) to achieve efficient classification, use it for recognition and solve inverse problems for recovery of network wiring. Furthermore, their combination enables predictive modeling of dynamic networks. I will describe the methodology and provide examples of real neurobiological systems for which the developed tools were applied. These include olfaction in moths, C elegans worm nervous system, and sun-compass navigation in Monarch butterflies.