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 [1].
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. |