We apply Bayesian Optimization techniques that improve information transfer when using visual stimulation.
We investigate techniques for efficient modeling and computation of latent spaces in neural and behavioral data.
We develop models for real-time analysis of signals for brain-computer interfaces that incorporate neural stimulations.
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.
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.