RatInABox: A toolkit for modelling locomotion and neuronal activity in continuous environments

Abstract

AbstractGenerating synthetic locomotory and electrophysiological data is a useful yet cumbersome step commonly required to study theoretical models of the brain’s role in spatial navigation. This process can be time consuming and, without a common framework, makes it difficult to reproduce or compare studies which each generate test data in different ways. In response we present RatInABox, an open-source Python toolkit designed to model realistic rodent locomotion and generate synthetic electrophysiological data from spatially modulated cell types. This software provides users with (i) the ability to construct one- or two-dimensional environments with configurable barriers and rewards, (ii) a realistic motion model for random foraging fitted to experimental data, (iii) rapid online calculation of neural data for many of the known self-location or velocity selective cell types in the hippocampal formation (including place cells, grid cells, boundary vector cells, head direction cells) and (iv) a framework for constructing custom cell types as well as multi-layer network models. Cell activity and the motion model are spatially and temporally continuous and topographically sensitive to boundary conditions and walls. We demonstrate that out-of-the-box parameter settings replicate many aspects of rodent foraging behaviour such as velocity statistics and the tendency of rodents to over-explore walls. Numerous tutorial scripts are provided, including examples where RatInABox is used for decoding position from neural data or to solve a navigational reinforcement learning task. We hope this tool significantly streamlines the process of theory-driven research into the brain’s role in navigation.