Image credit: niki_emmert (CC0)
The brain is a complex system made up of over 100 billion neurons that interact to give rise to all sorts of behaviours. To understand how neural interactions enable distinct behaviours, neuroscientists often build computational models that can reproduce some of the interactions and behaviours observed in the brain.
Unfortunately, good computational models can be hard to build, and it can be wasteful for different groups of scientists to each write their own software to model a similar system. Instead, it is more effective for scientists to share their code so that different models can be quickly built from an identical set of core elements. These toolkits should be well made, free and easy to use.
One of the largest fields within neuroscience and machine learning concerns navigation: how does an organism – or an artificial agent – know where they are and how to get where they are going next? Scientists have identified many different types of neurons in the brain that are important for navigation. For example, ‘place cells’ fire whenever the animal is at a specific location, and ‘head direction cells’ fire when the animal's head is pointed in a particular direction. These and other neurons interact to support navigational behaviours.
Despite the importance of navigation, no single computational toolkit existed to model these behaviours and neural circuits. To fill this gap, George et al. developed RatInABox, a toolkit that contains the building blocks needed to study the brain’s role in navigation. One module, called the ‘Environment’, contains code for making arenas of arbitrary shapes. A second module contains code describing how organisms or ‘Agents’ move around the arena and interact with walls, objects, and other agents. A final module, called ‘Neurons’, contains code that reproduces the reponse patterns of well-known cell types involved in navigation. This module also has code for more generic, trainable neurons that can be used to model how machines and organisms learn.
Environments, Agents and Neurons can be combined and modified in many ways, allowing users to rapidly construct complex models and generate artificial datasets. A diversity of tutorials, including how the package can be used for reinforcement learning (the study of how agents learn optimal motions) are provided.
RatInABox will benefit many researchers interested in neuroscience and machine learning. It is particularly well positioned to bridge the gap between these two fields and drive a more brain-inspired approach to machine learning. RatInABox’s userbase is fast growing, and it is quickly becoming one of the core computational tools used by scientists to understand the brain and navigation. Additionally, its ease of use and visual clarity means that it can be used as an accessible teaching tool for learning about spatial representations and navigation.
