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redcliff-s-hypothesizing-dynamic-causal-graphs
redcliff-s-hypothesizing-dynamic-causal-graphs PublicCode corresponding to ICML 2025 paper "Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series"
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- VMD-and-NAMD-Connexin-Protein-Simulation-Protocol Public
This document provides a streamlined workflow for running molecular dynamics (MD) simulations using VMD (Visual Molecular Dynamics) and NAMD (Nanoscale Molecular Dynamics). It is intended as a practical guide and reproducible framework for researchers, students, and developers working in computational chemistry, biophysics, or structural biology.
carlson-lab/VMD-and-NAMD-Connexin-Protein-Simulation-Protocol’s past year of commit activity - redcliff-s-hypothesizing-dynamic-causal-graphs Public
Code corresponding to ICML 2025 paper "Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series"
carlson-lab/redcliff-s-hypothesizing-dynamic-causal-graphs’s past year of commit activity - Social_Projections Public
Original code and model parameters for social network projections from the Social Appetitive Brain State paper.
carlson-lab/Social_Projections’s past year of commit activity - OptoLinCx Public
carlson-lab/OptoLinCx’s past year of commit activity - lpne-data-analysis Public
carlson-lab/lpne-data-analysis’s past year of commit activity - dCSFA-NMF Public
dCSFA-NMF contains pytorch code for Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity
carlson-lab/dCSFA-NMF’s past year of commit activity
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