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RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments
- Source :
- eLife, Vol 13 (2024)
- Publication Year :
- 2024
- Publisher :
- eLife Sciences Publications Ltd, 2024.
-
Abstract
- Generating synthetic locomotory and neural 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 neural 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 visual cues, (ii) a physically realistic random motion model 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, multi-layer network models and data- or policy-controlled motion trajectories. The motion and neural models are spatially and temporally continuous as well as 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 will significantly streamline computational research into the brain’s role in navigation.
Details
- Language :
- English
- ISSN :
- 2050084X
- Volume :
- 13
- Database :
- Directory of Open Access Journals
- Journal :
- eLife
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.1d9ac20d0c7430597ba67028d269ecc
- Document Type :
- article
- Full Text :
- https://doi.org/10.7554/eLife.85274