A summary of the current “state-of-the-field” of single-molecule FRET used for probing biomolecular structural dynamics.

An inference paradigm for extracting neuronal correlations from two-photon imaging data, without requiring intermediate spike deconvolution, provides significant performance gains over existing methods as demonstrated by theoretical analysis, simulation studies, and real-data applications.

Emergent property inference, a novel machine learning methodology, learns distributions of neural circuit model parameters that produce computational properties and provides novel scientific insight through the quantification of the rich parametric structure it captures.