NeuralPlayground: A Standardised Environment for Evaluating Models of Hippocampus and Entorhinal Cortex

The abstract representation of space has been extensively studied in the hippocampus and entorhinal cortex. Yet while a growing variety of theoretical models have been proposed to capture the rich neural and behavioral phenomenology associated with these regions, it remains difficult to compare these theories systematically against each other using the full range of empirical data. To address this gap, we built an open-source standardised software framework, named NeuralPlayground, to facilitate comparisons of hippocampus and entorhinal cortex models. This Python software package offers a reproducible way to compare models consistently against a centralised library of published experimental results, including neural recordings and animal behavior. The framework currently contains implementations of three Agents, including a excitatory/inhibitory plasticity agent and the Tolman-Eichenbaum machine (TEM), three Experiments providing simple interfaces to publicly available neural and behavioral data; a customizable 2-dimensional Arena (continuous and discrete) able to produce common experimental environments such as T-maze, circular and dynamical arenas which the agents can move in and interact with, and a Comparison tool to facilitate systematic comparisons by enabling users to pick from any Agent, Arena, Experiments, and metrics to produce a comparison of the results between artificial agents and experimental measurements. As a result, we hope to generate new insights into the model theory and implementation, illuminating the strengths and weaknesses of each model. We hope our framework, available at github.com/anonymous, offers a foundation that the community will build upon, toward a shared computational understanding of the hippocampus and entorhinal cortex.