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In order to properly assess the function and computational properties ofsimulated neural systems, it is necessary to account for the nature of thestimuli that drive the system. However, providing stimuli that are rich and yetboth reproducible and amenable to experimental manipulations is technicallychallenging, and even more so if a closed-loop scenario is required. In thiswork, we present a novel approach to solve this problem, connecting roboticsand neural network simulators. We implement a middleware solution that bridgesthe Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC).This enables any robotic and neural simulators that implement the correspondinginterfaces to be efficiently coupled, allowing real-time performance for a widerange of configurations. This work extends the toolset available forresearchers in both neurorobotics and computational neuroscience, and createsthe opportunity to perform closed-loop experiments of arbitrary complexity toaddress questions in multiple areas, including embodiment, agency, andreinforcement learning.

Closed loop interactions between spiking neural network and robotic simulators based on MUSIC and ROS