This title appears in the Scientific Report :
2016
Please use the identifier:
http://dx.doi.org/10.3389/fninf.2016.00031 in citations.
Please use the identifier: http://hdl.handle.net/2128/12490 in citations.
Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS
Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS
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 technica...
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Personal Name(s): | Weidel, Philipp (Corresponding author) |
---|---|
Djurfeldt, Mikael / Morrison, Abigail / Duarte, Renato | |
Contributing Institute: |
Computational and Systems Neuroscience; INM-6 JARA-BRAIN; JARA-BRAIN Computational and Systems Neuroscience; IAS-6 |
Published in: | Frontiers in neuroinformatics, 10 (2016) S. 31 |
Imprint: |
Lausanne
Frontiers Research Foundation
2016
|
DOI: |
10.3389/fninf.2016.00031 |
PubMed ID: |
27536234 |
Document Type: |
Journal Article |
Research Program: |
SimLab Neuroscience W2/W3 Professorinnen Programm der Helmholtzgemeinschaft Neural network mechanisms of reinforcement learning Theory, modelling and simulation |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/12490 in citations.
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. |