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 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.

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.

On the Self-Repair Role of Astrocytes in STDP Enabled Unsupervised SNNs.

Neuromorphic computing is emerging to be a disruptive computational paradigm that attempts to emulate various facets of the underlying structure and functionalities of the brain in the algorithm and hardware design of next-generation machine learning platforms. This work goes beyond the focus of current neuromorphic computing architectures on computational models for neuron and synapse to examine other computational units of the biological brain that might contribute to cognition and especially self-repair. We draw inspiration and insights from computational neuroscience regarding functionalities of glial cells and explore their role in the fault-tolerant capacity of Spiking Neural Networks (SNNs) trained in an unsupervised fashion using Spike-Timing Dependent Plasticity (STDP). We characterize the degree of self-repair that can be enabled in such networks with varying degree of faults ranging from 50 to 90% and evaluate our proposal on the MNIST and Fashion-MNIST datasets.