Draelos Lab @

University of Michigan

Current Projects

Bayesian optimization for visual stimuli

We apply Bayesian Optimization techniques that improve information transfer when using visual stimulation.

Latent neural/behavioral modeling

We investigate techniques for efficient modeling and computation of latent spaces in neural and behavioral data.

Real-time BCI with neural stimulations

We develop models for real-time analysis of signals for brain-computer interfaces that incorporate neural stimulations.

Past Projects

Bubblewrap: Online tiling and real-time flow prediction on neural manifolds

We developed a new method for approximating dynamics as a probability flow between discrete tiles on a low-dimensional manifold. The model can be trained quickly and retains predictive performance many time steps into the future, and is fast enough to serve as a component of closed-loop causal experiments in neuroscience. Our recent preprint on this work can be found here.

Online neural connectivity estimation with noisy group testing

How can we get connectivity between large systems of neurons in vivo? Using stimulations of small ensembles and a statistical method called group testing, we show in our recent paper that this is now feasible even in networks of up to 10 thousand neurons.