demos

Demos

In this folder we provide numerous "example scripts" or "demos" which will help when learning RatInABox. All demos can be run in Google Colab but, for best performance, it is recommended to use a local IDE.

In approximate order of complexity, these include:

• simple_example.ipynb: a very simple tutorial for importing RiaB, initilising an Environment, Agent and some PlaceCells, running a brief simulation and outputting some data.
• extensive_example.ipynb: a more involved tutorial. More complex enivornment, more complex cell types and more complex plots are used.
• list_of_plotting_functions.md: All the types of plots available for are listed and explained.
• readme_figures.ipynb: (Almost) all plots/animations shown in the root readme are produced from this script (plus some minor formatting done afterwards in powerpoint).
• paper_figures.ipynb: (Almost) all plots/animations shown in the paper are produced from this script (plus some major formatting done afterwards in powerpoint).
• decoding_position_example.ipynb: Postion is decoded from neural data generated with RatInABox using linear regression. Place cells, grid cell and boundary vector cells are compared.
• conjunctive_gridcells_example.ipynb: GridCells and HeadDirectionCells are minimally combined useing FeedForwardLayer to create head-direction-selective grid cells (aka. conjunctive cells).
• splitter_cells_example.ipynb: A simple simultaion demonstrating how Splitter cell data could be create in a figure-8 maze.
• deep_learning_example.ipynb: Here we showcase NeuralNetworkNeurons, a class of Neurons which has a small neural network embedded inside. We train them to take grid cells as inputs and output an arbitrary function as their rate map.
• reinforcement_learning_example.ipynb: RatInABox is use to construct, train and visualise a small two-layer network capable of model free reinforcement learning in order to find a reward hidden behind a wall.
• actor_critic_example.ipynb: RatInABox is use to implement the actor critic algorithm using deep neural networks.
• successor_features_example.ipynb: RatInABox is use to learn successor features under random and biased motion policies.
• path_integration_example.ipynb: RatInABox is use to construct, train and visualise a large multi-layer network capable of learning a "ring attractor" capable of path integrating a position estimate using only velocity inputs.
• vector_cell_demo.ipynb: A demo for the Vector Cells (OVCs & BVCs) and how to simulate them with various parameters and different manifolds. Also includes a demo on how to pass custom manifolds for the cells.