I study how complex systems
adapt and remain stable.
I'm Thomas Richner, a research scientist at Mayo Clinic. I combine mathematical analysis with numerical experiments to investigate learning, adaptation, and recurrent systems.
Mathematical models. Numerical experiments.
Systems built to test ideas.
I investigate how adaptation shapes effective connectivity and stability in recurrent networks. My current research asks how networks can learn through local rules while maintaining stable dynamics.
Read the paper ↗ Explore the code ↗
Using Gaussian process models and Bayesian optimization to guide the search for effective stimulation parameters in a closed-loop seizure-control system.
Read the paper ↗
Developing transparent micro-electrocorticography arrays and integrating electrical recordings with optogenetics and advanced photonics.
Read the paper ↗My broader research interests concern how intelligent systems learn and adapt—particularly continual learning, local learning, and efficient recurrent architectures.
Continual and federated learning. Preserving useful behavior as systems adapt.
Stability in recurrent networks with local learning. The relationship between optimization and dynamics.
Compositional architectures, Bayesian methods, and efficient ways to explore difficult problems.
My background spans mathematics, computational modeling, and engineering. Across research in optimization, neural dynamics, and neural interfaces, I move between theory, numerical methods, and experiments.
At Mayo Clinic, I investigate network stability and use electrical brain stimulation for system identification. My broader interests connect this work to foundational questions in AI.