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Three Tools for the Real-Time Simulation of Embodied Spiking Neural Networks Using GPUs

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Abstract

This paper presents a toolbox of solutions that enable the user to construct biologically-inspired spiking neural networks with tens of thousands of neurons and millions of connections that can be simulated in real time, visualized in 3D and connected to robots and other devices. NeMo is a high performance simulator that works with a variety of neural and oscillator models and performs parallel simulations on either GPUs or multi-core processors. SpikeStream is a visualization and analysis environment that works with NeMo and can construct networks, store them in a database and visualize their activity in 3D. The iSpike library provides biologically-inspired conversion between real data and spike representations to support work with robots, such as the iCub. Each of the tools described in this paper can be used independently with other software, and they also work well together.

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Notes

  1. PyNN (Davison et al. 2008) is a common SNN simulator interface written in Python. By default PyNN is supported by several simulators including NEURON (Carnevale and Hines 2006), NEST (Gewaltig and Diesmann 2007), and Brian (Goodman and Brette 2009). PyNN support for NeMo is currently found in its development branch.

  2. The Matlab and Python interfaces also provide vector versions of the network construction functions.

  3. Both SpikeStream and PyNN provides such connectivity patterns, for example, to create topographic connections.

  4. Another application called SpikeStream was created after the one described in this paper and given this name independently: http://spikestream.bitbucket.org. ‘SpikeStream’ has also been used to name a component of the C2 simulator (Ananthanarayanan et al. 2009).

  5. Instructions for writing SpikeStream plugins are given in the SpikeStream manual (Gamez 2011b).

  6. NRM stands for Neural Representation Modeller, a simulator of weightless neurons that was developed by Barry Dunmall and Igor Aleksander (Aleksander 2005).

  7. Player/Stage: http://playerstage.sf.net; Orocos: http://www.orocos.org; Urbi: http://gostai.com/products/urbi/; Robot Operating System (ROS): http://www.ros.org. While iSpike’s YARP interface should make it relatively straightforward to interface with these other systems, it has only been tested with the iCub robot.

  8. Rods are not included in the current implementation of iSpike because it is designed for daylight conditions, during which the light intensity is too high for rod photoreceptors to operate.

  9. The neuron that receives the most current will be the first to spike and it will also fire at the highest rate. The neuron with the most current will also be likely to fire before the one with the second highest current, and so on, producing a rank order code.

  10. These effects relate to changes to the temporal grid on which the spike events take place. A reduction of the step size used in the numerical integration within each simulation time step would only have a minimal effect on performance.

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Acknowledgements

This work was supported by EPSRC grant EP/F033516/1. Some of the GPUs used were funded through the NVIDIA University Program. We would also like to thank the reviewers of this paper for their helpful comments.

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  1. Imperial College London, 180 Queen’s Gate, London, SW7 2AZ, UK

    Andreas K. Fidjeland, David Gamez, Murray P. Shanahan & Edgars Lazdins

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  1. Andreas K. Fidjeland
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Fidjeland, A.K., Gamez, D., Shanahan, M.P. et al. Three Tools for the Real-Time Simulation of Embodied Spiking Neural Networks Using GPUs. Neuroinform 11, 267–290 (2013). https://doi.org/10.1007/s12021-012-9174-x

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