RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments

被引:1
|
作者
George, Tom M. [1 ]
Rastogi, Mehul [1 ]
de Cothi, William John [2 ]
Clopath, Claudia [1 ,3 ]
Stachenfeld, Kimberly [4 ,5 ]
Barry, Caswell [2 ]
Mathis, Mackenzie W.
机构
[1] UCL, Sainsbury Wellcome Ctr, London, England
[2] UCL, Dept Cell & Dev Biol, London, England
[3] Imperial Coll London, Dept Bioengn, London, England
[4] Google DeepMind, London, England
[5] Columbia Univ, New York, NY USA
来源
ELIFE | 2024年 / 13卷
关键词
hippocampus; locomotion; neural data; trajectory; software; open source; GEOMETRIC DETERMINANTS; PLACE FIELDS; REPRESENTATION; HIPPOCAMPUS; DIRECTION; CELLS;
D O I
10.7554/eLife.85274
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:35
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