Dynamical systems reconstruction

For decades dynamical systems theory played a pivotal role in theoretical and computational neuroscience, as it links biophysical and biochemical processes to neural computation. In fact, dynamical systems are computationally universal. Rather than hand-crafting computational theories of neural function based on dynamical systems, recent developments in scientific machine learning (ML) and AI suggest that we may be able to infer such dynamical-computational models directly from neurophysiological and behavioral observations. This is called dynamical systems reconstruction (DSR), the learning o

Updating behavioral policies in novel environments or upon shifting action-outcome contingencies is essential for survival. A number of studies have identified the medial prefrontal cortex (mPFC) as a key player in cognitive flexibility, rule learning and uncertainty detection. Animals learning a new behavioral paradigm, or switching between rules, show abrupt changes in their performance, accompanied by sharp transitions in neuronal population dynamics. However, the precise mechanisms regulating behavioral flexibility remain unclear. Here we aim to extract the computational mechanisms behind