RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments
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 g...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2024-02-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/85274 |
| _version_ | 1827352986807959552 |
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| author | Tom M George Mehul Rastogi William de Cothi Claudia Clopath Kimberly Stachenfeld Caswell Barry |
| author_facet | Tom M George Mehul Rastogi William de Cothi Claudia Clopath Kimberly Stachenfeld Caswell Barry |
| author_sort | Tom M George |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T03:18:03Z |
| format | Article |
| id | doaj.art-d1d9ac20d0c7430597ba67028d269ecc |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-03-08T03:18:03Z |
| publishDate | 2024-02-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-d1d9ac20d0c7430597ba67028d269ecc2024-02-12T14:45:33ZengeLife Sciences Publications LtdeLife2050-084X2024-02-011310.7554/eLife.85274RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environmentsTom M George0https://orcid.org/0000-0002-4527-8810Mehul Rastogi1William de Cothi2Claudia Clopath3https://orcid.org/0000-0003-4507-8648Kimberly Stachenfeld4Caswell Barry5Sainsbury Wellcome Centre, University College London, London, United KingdomSainsbury Wellcome Centre, University College London, London, United KingdomDepartment of Cell and Developmental Biology, University College London, London, United KingdomSainsbury Wellcome Centre, University College London, London, United Kingdom; Department of Bioengineering, Imperial College London, London, United KingdomGoogle DeepMind, London, United Kingdom; Columbia University, New York, United StatesDepartment of Cell and Developmental Biology, University College London, London, United KingdomGenerating 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.https://elifesciences.org/articles/85274hippocampuslocomotionneural datatrajectorysoftwareopen source |
| spellingShingle | Tom M George Mehul Rastogi William de Cothi Claudia Clopath Kimberly Stachenfeld Caswell Barry RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments eLife hippocampus locomotion neural data trajectory software open source |
| title | RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments |
| title_full | RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments |
| title_fullStr | RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments |
| title_full_unstemmed | RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments |
| title_short | RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments |
| title_sort | ratinabox a toolkit for modelling locomotion and neuronal activity in continuous environments |
| topic | hippocampus locomotion neural data trajectory software open source |
| url | https://elifesciences.org/articles/85274 |
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