Pengcheng Zhou

TL;DR: CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection, while requiring minimal user intervention.

TL;DR: A new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest is described, which substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes.

TL;DR: The algorithm is a generalization of the pool adjacent violators algorithm (PAVA) for isotonic regression and inherits its linear-time computational complexity and gains remarkable increases in processing speed: more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods.

TL;DR: The hippocampus encodes not only neutral but also valence-related contextual information, and the vCA1-LHA pathway is a direct route by which the hippocampus can rapidly influence innate anxiety behavior.

TL;DR: It is found that SPN ensembles active during specific actions were spatially closer and more correlated overall and the accuracy of decoding behavior from SPN ensemble patterns was directly related to the dissimilarity between behavioral clusters.