ROS-MUSIC toolchain for spiking neural network simulations in a robotic environment
Weidel et al. BMC Neuroscience
ROS-MUSIC toolchain for spiking neural network simulations in a robotic environment
Philipp Weidel 2 3
Renato Duarte 2 3
Karolína Korvasová 2 3
Jenia Jitsev 2 3
Abigail Morrison 0 1 2 3
0 Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center , Jülich , Germany
1 Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum , 44801 Bochum , Germany
2 Authors' details
3 Institute of Advanced Simulation (IAS-6) & Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Juelich , 52425 Juelich , Germany
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From 24th Annual Computational Neuroscience Meeting: CNS*2015
Prague, Czech Republic. 18-23 July 2015
Studying a functional, biologically plausible neural
network 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,
providing 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
neuroscience with the goal of connecting robotic simulators
with neural simulators. This toolchain is a generic
solution 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
communicator between the major, state-of-the-art neural
simulators: 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
simulator 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
simulator back to the robotic simulator, 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
Braitenberg [5] using the robotic simulator Gazebo and the
neural simulator NEST.
In future work, we aim to create a testbench,
consisting of various environments for reinforcement learning
algorithms, to provide a validation tool for the
functionality of biological motivated models of learning.
Mikael Djurfeldt , et al: Run-time interoperability between neuronal network simulators based on the MUSIC framework . Neuroinformatics 2010 , 8 .1: 43 - 60 .
Chris Eliasmith , Charles H. Anderson : Neural engineering: Computation, representation, and dynamics in neurobiological systems . MIT press 2004 .
Valentino Braitenberg : Vehicles: Experiments in synthetic psychology . MIT press 1986 . (...truncated)