Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
Authors: Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott Linderman, Liam Paninski
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate this framework on two different experimental paradigms using distinct behavioral and neural recording technologies. |
| Researcher Affiliation | Academia | Eleanor Batty*, Matthew R Whiteway*, Shreya Saxena, Dan Biderman, Taiga Abe Columbia University erb2180,m.whiteway,ss5513,db3236,ta2507 @columbia.edu Simon Musall Cold Spring Harbor EMAIL Winthrop Gillis Harvard Medical School EMAIL Jeffrey E Markowitz Harvard Medical School EMAIL Anne Churchland Cold Spring Harbor EMAIL John Cunningham Columbia University EMAIL Sandeep Robert Datta Harvard Medical School EMAIL Scott W Linderman Stanford University EMAIL Liam Paninski Columbia University EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | A python implementation of our pipeline is available at https://github.com/ebatty/behavenet, which is based on the PyTorch [46], ssm [47], and Test Tube [48] libraries. |
| Open Datasets | Yes | Widefield Calcium Imaging (WFCI) dataset [8, 19]. ... Neuropixels (NP) dataset [9, 18]. |
| Dataset Splits | Yes | Training terminates when MSE on held-out validation data, averaged over the previous 10 epochs, begins to increase. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | A python implementation of our pipeline is available at https://github.com/ebatty/behavenet, which is based on the PyTorch [46], ssm [47], and Test Tube [48] libraries. Specific version numbers for these libraries are not provided. |
| Experiment Setup | Yes | We train the autoencoders by minimizing the mean squared error (MSE) between original and reconstructed frames using the Adam optimizer [39] with a learning rate of 10^-4. Models are trained for a minimum of 500 epochs and a maximum of 1000 epochs. Training terminates when MSE on held-out validation data, averaged over the previous 10 epochs, begins to increase. |