NeuralPlayground: A Standardised Environment for Evaluating Models of Hippocampus and Entorhinal Cortex
Bibliographic record
Abstract
Abstract Neural processes in the hippocampus and entorhinal cortex are thought to be crucial for spatial cognition. A growing variety of theoretical models have been proposed to capture the rich neural and behavioral phenomena associated with these circuits. However, systematic comparison of these theories, both against each other and against empirical data, remains challenging. To address this gap, we present NeuralPlayground, an open-source standardised software framework for comparisons between theory and experiment in the domain of spatial cognition. This Python software package offers a reproducible way to compare models against a centralised library of published experimental results, including neural recordings and animal behavior. The framework implements three Agents embodying different computational models; three Experiments comprising publicly available neural and behavioral datasets; a customisable 2-dimensional Arena (continuous and discrete) able to generate common and novel spatial layouts; and a Comparison tool that facilitates systematic comparisons between models and data. Each module can also be used separately, allowing standardised and flexible access to influential models and data sets. We hope NeuralPlayground, available on GitHub 3 , provides a starting point for a shared, standardized, open, and reproducible computational understanding of the role of the hippocampus and entorhinal cortex in spatial cognition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".