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This article is part of the supplement: Twentieth Annual Computational Neuroscience Meeting: CNS*2011

Open Access Poster presentation

Interfacing a parallel simulation of a neuronal network to robotic hardware using MUSIC, with application to real-time figure-ground segregation

Ali Nazem12, Gert Kootstra1, Danica Kragic1 and Mikael Djurfeldt23*

Author Affiliations

1 CVAP, CSC, KTH, 100 44 Stockholm, Sweden

2 PDC, CSC, KTH, 100 44 Stockholm, Sweden

3 INCF, Karolinska Institutet, Nobels väg 15A, 171 77 Stockholm, Sweden

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BMC Neuroscience 2011, 12(Suppl 1):P78  doi:10.1186/1471-2202-12-S1-P78


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/12/S1/P78


Published:18 July 2011

© 2011 Nazem et al; licensee BioMed Central Ltd.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

MUSIC, the multi-simulation coordinator, supports communication between neuronal-network simulators, or other (parallel) applications, running in a cluster super-computer [1,4]. Here, we've developed a MUSIC-enabled class library providing an interface between MUSIC-enabled applications and applications running on computers outside of the cluster. Specifically, we have used this component to interface the cameras of a robotic head to a neuronal-network simulation running on a Blue Gene/L supercomputer [2]. The interface enables the neuronal-network simulator to receive real-world images in real-time from the robot. Moreover, it enables the robot to be controlled by the neuronal network. The neuronal-network simulation implements a model of figure-ground segregation based on neuronal activity in the Macaque visual cortex [3].

I. A special purpose TCP/IP based communication interface has been implemented in C++ as an extendible class library. The architecture of the interface is shown in Figure 1. The client end of the interface is specifically designed to meet the requirements for operating on the Blue Gene /L, as well as being MUSIC-aware. The server side component resides in the outside world providing the client with streaming real-time data. We designed an inheritable class called CSerializable that defines the unit of data and marshals the data across the route from the source to the final destination in the parallel application. Based on CSerializable, we implemented the entities CCommand and CRawImage which are required for transmission of control commands and images, respectively, between the parallel application and the robot (Figure 1). A single process application called MusicGate, connects the client side of the interface and the music-enabled component together. The parallel application sends a command to the server requesting a stream of images. As soon as one frame of the data sent from the server is available in MusicGate, it will be directly transferred from the read buffer to the parallel application by the MUSIC library, hence avoiding redundant internal memory operations. The implemented architecture performs the communication IO operations in parallel with the neural processing in order to minimize the idle time in compute nodes. The idle time is a function of communication latency, and the processing load of the neuronal-network simulation.

II. Having the communication interface, we implemented a parallel model for figure-ground segregation. The implementation covers the structure of neurons, neuronal layers, and neuronal networks that can operate on a parallel platform. We defined the concept of a tile of neurons. One dedicated processor is allocated to each tile of neurons. Each layer contains a number of tiles.

Results

We have successfully implemented an interface between a neuronal-network simulator running on a parallel computer and a robot in the real world. The latency and transfer rate of the entire model makes real-time figure-ground segregation possible. Since the client side of the interface provides a standard MUSIC port towards the parallel application, it can be used to connect the robotic head to a generic MUSIC-enabled neuronal-network simulator. We demonstrate this for the NEST simulator [4].

thumbnailFigure 1. The MUSIC interface architecture.

References

  1. Örjan Ekeberg, Mikael Djurfeldt: MUSIC - Multi-Simulation Coordinator User’s Manual.

    2009.

  2. Mullen-Schultz Gary L: Blue Gene/L: Application Development.

    IBM 2006. OpenURL

  3. Roelfsema Pieter R, Lamme Victor AF, Henk Spekreijse, Holger Bosch: Figure–Ground Segregation in a Recurrent Network Architecture.

    Journal of Cognitive Neuroscience 2002, 14(4):525-537. PubMed Abstract | Publisher Full Text OpenURL

  4. Mikael Djurfeldt, Johannes Hjorth, Eppler Jochen M, Niraj Dudani, Moritz Helias, Potjans Tobias C, Bhalla Upinder S, Markus Diesmann, Kotaleski Jeanette Hellgren, Örjan Ekeberg: Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework.

    Neuroinform 2010. OpenURL