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..
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. |