Abstract
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.
Highlights
Studying a functional, biologically plausible neural network that performs a particular task is highly relevant for progress in both neuroscience and robotics
For the rate or Poissonian encoding mechanisms (Figures 2A,B), simulations of up to 150, 000 encoding neurons are possible in real-time when using Neural Simulation Tool (NEST) or when the toolchain does not include a neural simulator
For the Neural Engineering Framework (NEF) encoding mechanism (Figure 2C), simulations of up to 20, 000 encoding neurons are possible in real-time when using NEST or no neural simulator in the toolchain
Summary
Biologically plausible neural network that performs a particular task is highly relevant for progress in both neuroscience and robotics. The major focus on this topic in the field of robotics consists of using of neural networks of varying degrees of complexity for controlling robots. The study of simulated neural networks is paramount to gain a better understanding of the processes underlying learning/adaptation to complex environments and global behavior (e.g., Chorley and Seth, 2008). Neural network simulators do not typically include functionality for representing environments and sensory input. Most tasks used to test the function of a simulated neural network are hardcoded to represent
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