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].
Coordinated hippocampal-entorhinal replay as structural inference
Authors: Talfan Evans, Neil Burgess
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Predictions from our hypothesis are evaluated by comparison to existing experimental data by both numerical simulations and theoretical analyses. |
| Researcher Affiliation | Academia | Talfan Evans Institute of Cognitive Neuroscience University College London EMAIL Neil Burgess Institute of Cognitive Neuroscience University College London EMAIL |
| Pseudocode | No | The paper describes algorithms using mathematical equations and textual descriptions, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of open-source code or links to a code repository. |
| Open Datasets | No | The paper describes simulated environments ('circular 1D track') and data generation process, but does not provide access information (link, citation, or name of a public dataset) for any publicly available or open dataset used. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits or reference predefined splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with versions). |
| Experiment Setup | Yes | A simple error-based learning rule with learning rate α = 1e-4 minimizes the error between the observation and movement models (Fig. 1B): 1 α d B dt = 2p t (G t Pt B) where σPI scales the noise with distance travelled. |