Richard Gao

Prof. Richard Gao

W1 Professor, Machine Learning of World Models

Faculty of Computer Science and Mathematics
Center for Cognition and Computation

Robert-Mayer-Straße 11-15, Room 103
Goethe University Frankfurt

Scientific Focus

We develop computational models and machine learning methods to uncover principles of organized brain dynamics from data: How do we describe them, where do they come from, and what are they useful for?

Our goal is to understand the circuit mechanisms and computational functions of organized brain dynamics by analyzing neural recordings—including population spiking, local field potentials, and invasive and non-invasive EEG—bridging investigations at multiple levels from neurobiology to cognition.

In the long-term, we hope to uncover general principles of neural dynamics, and to create theoretical and computational tools that can inform translational research and clinical applications.

Methods

probabilistic machine learning, mechanistic models, generative models, statistical data analysis, signal processing

Selected Publications

Gao, R., Deistler, M., Schulz, A., Gonçalves, P. J., & Macke, J. H. (2024). Deep inverse modeling reveals dynamic-dependent invariances in neural circuit mechanisms. bioRxiv.

Zeraati, R., Levina, A., Macke, J. H., & Gao, R. (2024). Neural timescales from a computational perspective. arXiv (to appear in Nature Neuroscience)

Vetter, J., Macke, J. H.*, & Gao, R.* (2024). Generating realistic neurophysiological time series with denoising diffusion probabilistic models. Patterns.

Gao, R.*, Deistler, M.*, & Macke, J. H. (2023). Generalized Bayesian inference for scientific simulators via amortized cost estimation. NeurIPS.

Gao, R., van den Brink, R. L., Pfeffer, T., & Voytek, B. (2020). Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture. eLife.