Closed loop interactions between spiking neural network and robotic simulators based on MUSIC and ROS
This incremental work provides a tool for researchers in neurorobotics and computational neuroscience to conduct closed-loop experiments on embodiment and reinforcement learning.
The authors tackled the challenge of providing rich, reproducible stimuli for simulated neural systems in closed-loop scenarios by developing a middleware that connects robotic and neural network simulators via ROS and MUSIC, enabling real-time performance for complex experiments.
In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC). This enables any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations. This work extends the toolset available for researchers in both neurorobotics and computational neuroscience, and creates the opportunity to perform closed-loop experiments of arbitrary complexity to address questions in multiple areas, including embodiment, agency, and reinforcement learning.