Gal Mishne
University of California, San Diego
69 Papers
80 Citations
Gal Mishne is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Computer science & Nonlinear dimensionality reduction. The author has an hindex of 9, co-authored 43 publications. Previous affiliations of Gal Mishne include Yale University & Technion – Israel Institute of Technology.
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Papers
Hierarchical Coupled-Geometry Analysis for Neuronal Structure and Activity Pattern Discovery
TL;DR: A new hierarchical coupled-geometry analysis that implicitly takes into account the connectivity structures between neurons and the dynamic patterns at multiple time scales is proposed, which gives rise to the joint organization of neurons and dynamic patterns in data-driven hierarchical data structures.
Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics
TL;DR: In this paper, a shared, robust, and interpretable low-dimensional space of brain dynamics can be recovered from a rich repertoire of task-based functional magnetic resonance imaging (fMRI) data.
Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior.
Hadas Benisty,Daniel Barson,Andrew H. Moberly,Sweyta Lohani,L. Tang,Ronald R. Coifman,Michael C Crair,Gal Mishne,Jessica A. Cardin,Michael J. Higley +9 more
TL;DR: Wide-field mesoscopic calcium imaging is used to monitor cortical dynamics in awake mice and an approach to quantify rapidly time-varying functional connectivity is developed, which provides insight into the relationship between neural signals and behavior.
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Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data
George Armstrong,Gibraan Rahman,Cameron Martino,Daniel McDonald,Antonio Gonzalez,Gal Mishne,Rob Knight +6 more
TL;DR: The need for further development in the field is described, in particular combining the power of phylogenetic analysis with the ability to handle sparsity, compositionality, and non-normality, as well as discussing current techniques that should be applied more widely in future analyses.
Smooth graph learning for functional connectivity estimation.
TL;DR: In this paper, the smooth graph functional connectivity (SGFC) framework is proposed to learn a graph representation of functional connectivity from functional magnetic resonance imaging (fMRI) signals, which can be used to predict fluid intelligence.
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