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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.
- 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"
- Social_Projections Public
Original code and model parameters for social network projections from the Social Appetitive Brain State paper.
- OptoLinCx Public
- lpne-data-analysis Public
- dCSFA-NMF Public
dCSFA-NMF contains pytorch code for Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity
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