Interpretable models

…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.

…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

The best current models of visual cortical neural responses rely on internal representations of large deep neural networks (DNNs) trained for object recognition. However, the inner workings of these DNN models are mostly uninterpretable due to their massive size—tens of millions of parameters. Do we need such large models in the first place? In this work, we focused on modeling visual cortical neurons in macaque mid-level visual area V4. We used ensemble learning and closed-loop experiments with active learning to train a DNN model to accurately predict V4 responses. However, our model had 90