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..
Distinguishing Learning Rules with Brain Machine Interfaces
Authors: Jacob Portes, Christian Schmid, James M Murray
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We model a cursor-control BMI task using recurrent neural networks, showing that learning rules can be distinguished in simulated experiments using only observations that a neuroscience experimenter would plausibly have access to. All simulations were run on a CPU |
| Researcher Affiliation | Academia | Jacob P. Portes Center for Theoretical Neuroscience Columbia University EMAIL; Christian Schmid Institute of Neuroscience University of Oregon EMAIL; James M. Murray Institute of Neuroscience University of Oregon EMAIL |
| Pseudocode | No | The paper describes learning rules with equations but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | Code included as supplementary material |
| Open Datasets | No | The paper describes simulating and training RNNs for a cursor-control task. It does not use a pre-existing, publicly available dataset with concrete access information; the data is generated through simulation. |
| Dataset Splits | No | The paper describes pretraining and then observing activity during early and late phases of learning. It does not mention explicit training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | All simulations were run on a CPU |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the general setup for simulations (e.g., pretraining, changing decoder, training with SL/RL) but does not provide specific hyperparameters like learning rates, batch sizes, or optimizer settings in the main text. |