Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Extracting computational mechanisms from neural data using low-rank RNNs
Authors: Adrian Valente, Jonathan W. Pillow, Srdjan Ostojic
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 19,036 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,939 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here we first demonstrate the consistency of our method and then apply it to two use cases: (i) we reverse-engineer black-box vanilla RNNs trained to perform cognitive tasks, and (ii) we infer latent dynamics and neural contributions from electrophysiological recordings of nonhuman primates performing a similar task. |
| Researcher Affiliation | Academia | Adrian Valente École Normale Supérieure PSL Research University Jonathan W. Pillow Princeton Neuroscience Institute Princeton University Srdjan Ostojic École Normale Supérieure PSL Research University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/adrian-valente/lowrank_inference/ |
| Open Datasets | Yes | We considered here electrophysiological recordings from non-human primates performing a context-dependent decision-making task similar to that studied in previous sections [29]. |
| Dataset Splits | Yes | The quality of fits was quantified by leaving out a random subset of 8 conditions during network inference, and evaluating the R2 of fitted networks on these left-out conditions. |
| Hardware Specification | Yes | All networks were trained using a single Nvidia GPU (RTX 3090, VRAM 24GB). |
| Software Dependencies | No | All networks were implemented in pytorch [34]. While it mentions PyTorch, it does not specify the version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | The networks were trained using the Adam optimizer with a learning rate of 10−3 and a batch size of 64. |