Figure 1. RatInABox is a flexible toolkit for simulating locomotion and neural data in complex continuous environments.

(a) One minute of motion in a 2D Environment with a wall. By default the Agent follows a physically realistic random motion model fitted to experimental data. (b) Premade neuron models include the most commonly observed position/velocity selective cells types (6 of which are displayed here). Users can also build more complex cell classes based on these primitives. Receptive fields interact appropriately with walls and boundary conditions. (c) As the Agent explores the Environment, Neurons generate neural data. This can be extracted for downstream analysis or visualised using in-built plotting functions. Solid lines show firing rates, and dots show sampled spikes. (d) One minute of random motion in a 1D environment with solid boundary conditions. (e) Users can easily construct complex Environments by defining boundaries and placing walls, holes and objects. Six example Environments, some chosen to replicate classic experimental set-ups, are shown here.