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ros music toolchain for spiking neural network simulations in a robotic environment

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Tiêu đề ROS Music Toolchain for Spiking Neural Network Simulations in a Robotic Environment
Tác giả Philipp Weidel, Renato Duarte, Karolína Korvasová, Jenia Jitsev, Abigail Morrison
Trường học Forschungszentrum Juelich
Chuyên ngành Computational Neuroscience, Robotics, Neural Simulation
Thể loại poster presentation
Năm xuất bản 2015
Thành phố Prague
Định dạng
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POSTER PRESENTATION Open AccessROS-MUSIC toolchain for spiking neural network simulations in a robotic environment Philipp Weidel1*, Renato Duarte1, Karolína Korvasová1, Jenia Jitsev1, A

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POSTER PRESENTATION Open Access

ROS-MUSIC toolchain for spiking neural network simulations in a robotic environment

Philipp Weidel1*, Renato Duarte1, Karolína Korvasová1, Jenia Jitsev1, Abigail Morrison1,2,3

From 24th Annual Computational Neuroscience Meeting: CNS*2015

Prague, Czech Republic 18-23 July 2015

Studying a functional, biologically plausible neural

net-work that performs a particular task is highly relevant for

progress in both neuroscience and machine learning

Most tasks used to test the function of a simulated neural

network are still very artificial and thus too narrow,

pro-viding only little insight into the true value of a particular

neural network architecture under study For example,

many models of reinforcement learning in the brain rely

on a discrete set of environmental states and actions [1]

In order to move closer towards more realistic models,

modeling studies have to be conducted in more realistic

environments that provide complex sensory input about

the states A way to achieve this is to provide an interface

between a robotic and a neural network simulation, such

that a neural network controller gains access to a realistic

agent which is acting in a complex environment that can

be flexibly designed by the experimentalist

To create such an interface, we present a toolchain,

consisting of already existing and robust tools, which

forms the missing link between robotic and

neu-roscience with the goal of connecting robotic simulators

with neural simulators This toolchain is a generic

solu-tion and is able to combine various robotic simulators

with various neural simulators by connecting the Robot

Operating System (ROS) [2] with the Multi-Simulation

Coordinator (MUSIC) [3] ROS is the most widely used

middleware in the robotic community with interfaces

for robotic simulators like Gazebo, Morse, Webots, etc,

and additionally allows the users to specify their own

robot and sensors in great detail with the Unified Robot

Description Language (URDF) MUSIC is a

communica-tor between the major, state-of-the-art neural

simula-tors: NEST, Moose and NEURON By implementing an

interface between ROS and MUSIC, our toolchain is combining two powerful middlewares, and is therefore a multi-purpose generic solution

One main purpose is the translation from continuous sensory data, obtained from the sensors of a virtual robot, to spiking data which is passed to a neural simu-lator of choice The translation from continuous data to spiking data is performed using the Neural Engineering Framework (NEF) proposed by Eliasmith & Anderson [4] By sending motor commands from the neural simu-lator back to the robotic simusimu-lator, the interface is forming a closed loop between the virtual robot and its spiking neural network controller

To demonstrate the functionality of the toolchain and the interplay between all its different components, we implemented one of the vehicles described by Braiten-berg [5] using the robotic simulator Gazebo and the neural simulator NEST

In future work, we aim to create a testbench, consist-ing of various environments for reinforcement learnconsist-ing algorithms, to provide a validation tool for the function-ality of biological motivated models of learning

Authors’ details

1 Institute of Advanced Simulation (IAS-6) & Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Juelich, 52425 Juelich, Germany.

2 Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum, 44801 Bochum, Germany.3Simulation Laboratory Neuroscience -Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center, Jülich, Germany.

Published: 18 December 2015 References

1 Jenia Jitsev, Morrison Abigail, Tittgemeyer Marc: Learning from positive and negative rewards in a spiking neural network model of basal ganglia Neural Networks (IJCNN), The 2012 International Joint Conference on IEEE 2012.

2 Morgan Quigley, et al: “ROS: an open-source Robot Operating System.” ICRA workshop on open source software 2009, 3(3.2).

* Correspondence: p.weidel@fz-juelich.de

1

Institute of Advanced Simulation (IAS-6) & Institute of Neuroscience and

Medicine (INM-6), Forschungszentrum Juelich, 52425 Juelich, Germany

Full list of author information is available at the end of the article

Weidel et al BMC Neuroscience 2015, 16(Suppl 1):P169

http://www.biomedcentral.com/1471-2202/16/S1/P169

© 2015 Weidel et al This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http:// creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/ zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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3 Mikael Djurfeldt, et al: Run-time interoperability between neuronal

network simulators based on the MUSIC framework Neuroinformatics

2010, 8.1:43-60.

4 Chris Eliasmith, Charles H Anderson: Neural engineering: Computation,

representation, and dynamics in neurobiological systems MIT press 2004.

5 Valentino Braitenberg: Vehicles: Experiments in synthetic psychology MIT

press 1986.

doi:10.1186/1471-2202-16-S1-P169

Cite this article as: Weidel et al.: ROS-MUSIC toolchain for spiking neural

network simulations in a robotic environment BMC Neuroscience 2015

16(Suppl 1):P169.

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Weidel et al BMC Neuroscience 2015, 16(Suppl 1):P169

http://www.biomedcentral.com/1471-2202/16/S1/P169

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