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.
A software platform for construction and orchestration of adaptive experiments, improv has been used to analyze streaming two-photon calcium images in larval zebrafish in real time and to fit GLM models to infer weighted connections among neurons as the experiment is ongoing. The codebase is open-source and freely available at github.com/pearsonlab/improv, and the associated paper can be found here.
First observation of complex supercurrent flow in a four-terminal graphene Josephson junction, where we find an unusual coexistence of supercurrent and dissipative currents in the same location. The paper can be found here.
