Deep Learning of Dynamical Systems for Mechanistic Insight and Prediction in Psychiatry
Explore deep learning techniques for analyzing dynamical systems in computational psychiatry through this 58-minute lecture by Daniel Durstewitz from Ruprecht-Karls-Universität Heidelberg. Delve into the application of dynamical systems theory in understanding neural and behavioral phenomena, focusing on the use of deep generative recurrent neural networks to infer dynamical systems from empirical data. Learn about the potential of these AI-based tools in constructing generative models of individual brains and behaviors, obtaining diagnostic markers, forecasting disease trajectories, and simulating therapeutic interventions. Examine example applications on fMRI and ecological momentary assessment data, and gain insights into topics such as attractor states, limit cycles, and the role of dynamical systems in psychiatric states. Discover how recurrent neural networks can be used to reconstruct dynamical systems, and explore statistical inference methods for both small and big data scenarios. Investigate the challenges in predicting dynamical systems and the importance of capturing multiple time scales in neuronal representations. Read more