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Full-scale simulation of a cortical microcircuit on SpiNNaker

Sacha J. Van Albada1*, Andrew G. Rowley2, Michael Hopkins2, Maximilian Schmidt1, Johanna Senk1, Alan B. Stokes2, Francesco Galluppi3, Dave R. Lester2, Markus Diesmann1, 4, 5 and Steve B. Furber2
  • 1 Research Center Jülich, Institute of Neuroscience and Medicine and Institute for Advanced Simulation, Germany
  • 2 University of Manchester, School of Computer Science, United Kingdom
  • 3 Université Pierre et Marie Curie, Unité Mixte de Recherche S968 Inserm, l'Université Pierre et Marie Curie, Centre National de la Recherche Scientifique Unité Mixte de Recherche 7210, Centre Hospitalier National d'Ophtalmologie des quinze-vingts, Vision Institute, France
  • 4 RWTH Aachen University, Medical Faculty, Germany
  • 5 RWTH Aachen University, Faculty I, Germany

SpiNNaker is a digital neuromorphic hardware designed to reduce simulation time and power consumption compared to traditional computing architectures. While it is able to simulate artificial neural networks with 1 ms resolution in real time, its performance for biologically realistic models necessitating shorter integration time steps and featuring a convergence of on the order of 10,000 synapses per neuron has not been fully tested. Furthermore, simulations on SpiNNaker have previously been downscaled compared to the biological numbers of neurons and synapses [1, 2]. We here describe the first full-scale simulations of a cortical microcircuit model [3] on SpiNNaker, comparing performance with that of NEST [4]. With approximately 80,000 leaky integrate-and-fire neurons and 0.3 billion synapses, this model is the largest simulated on SpiNNaker to date. The scale-up is enabled by recent developments in the SpiNNaker software stack (https://github.com/SpiNNakerManchester) that allow simulations to be spread across multiple boards. The implementation uses the simulator-independent description language PyNN [5]. We consider two types of NEST simulations: one with spikes constrained to a 0.1 ms grid, and one with spikes in continuous time. The comparison with the latter provides a sensitive test for the correctness of SpiNNaker after a major reorganization of its software stack. We describe deterministic comparisons of the single-neuron dynamics and statistical comparisons of the network dynamics, quantified in terms of firing-rate distributions, spiking irregularity, and correlations in the activity of different neurons. These comparisons reveal close correspondence of the NEST and SpiNNaker results, demonstrating the usability of SpiNNaker for large-scale networks with biological time scales.

Acknowledgements

This work was supported by the European Union (BrainScaleS, grant 269921). The design and construction of the SpiNNaker machine was supported by EPSRC (the UK Engineering and Physical Sciences Research Council) under grants EP/D07908X/1 and EP/G015740/1. Ongoing support comes from the EU ICT Flagship Human Brain Project (FP7-604102) and the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement 320689.

References

1. Bhattacharya, B. S., Patterson, C., Galluppi, F., Durrant, S. J., & Furber, S. (2014). Engineering a thalamo-cortico-thalamic circuit on SpiNNaker: a preliminary study toward modeling sleep and wakefulness. Frontiers in Neural Circuits, 8. doi: 10.3389/fncir.2014.00046
2. Sharp, T., Galluppi, F., Rast, A., & Furber, S. (2012). Power-efficient simulation of detailed cortical microcircuits on SpiNNaker. Journal of Neuroscience Methods, 210(1): 110-118. doi: 10.1016/j.jneumeth.2012.03.001
3. Potjans, T. C., & Diesmann, M. (2014). The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cerebral Cortex, 24(3): 785-806. doi: 10.1093/cercor/bhs358
4. Eppler, J. M. et al. (2015). NEST 2.8.0. Zenodo. 10.5281/zenodo.32969
5. Davison, A., Brüderle, D., Kremkow, J., Muller, E., Pecevski, D., Perrinet, L., & Yger, P. (2009). PyNN: a common interface for neuronal network simulators. Frontiers in Neuroinformatics, 2(11). doi: 10.3389/neuron.11.011.2008


Keywords: neuromorphic hardware, spiking neural networks, NEST, SpiNNaker, accuracy, large-scale simulation, Software, leaky integrate-and-fire model, PyNN

Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.

Presentation Type: Poster

Topic: Neuromorphic engineering

Citation: Van Albada SJ, Rowley AG, Hopkins M, Schmidt M, Senk J, Stokes AB, Galluppi F, Lester DR, Diesmann M and Furber SB (2016). Full-scale simulation of a cortical microcircuit on SpiNNaker. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00029

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Received: 28 Apr 2016; Published Online: 18 Jul 2016.

* Correspondence: Dr. Sacha J Van Albada, Research Center Jülich, Institute of Neuroscience and Medicine and Institute for Advanced Simulation, Jülich, 52425, Germany, s.van.albada@fz-juelich.de