Topic: interpretable models

Brain function and psychiatric symptoms evolve dynamically. Manuel Brenner presents self-learning mathematical models that infer hidden states and patterns from brain activity and behaviour. Developed within LOEWE DYNAMIC, the interpretable models connect artificial intelligence, basic neuroscience and clinical psychiatry, seeking a path from temporal-data modelling to clinical application.

Modern experimental techniques now reveal the structure and function of neural circuits at unprecedented scale and resolution. How can we use this wealth of data to understand how cells and circuits implement computations underlying behaviour? Achieving this goal requires models that are consistent with biophysical mechanisms and circuit dynamics, yet flexible enough to capture behaviourally relevant computations. We develop simulation-based machine learning methods that address this challenge. I will show how these approaches—in combination with connectomic measurements—make it possible to bu