Display options
Cite
Sen-Bhattacharya B, Serrano-Gotarredona T, Balassa L, et al. A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine. Front Neurosci. 2017;11:454doi: 10.3389/fnins.2017.00454.
Sen-Bhattacharya, B., Serrano-Gotarredona, T., Balassa, L., Bhattacharya, A., Stokes, A. B., Rowley, A., Sugiarto, I., & Furber, S. (2017). A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine. Frontiers in neuroscience, 11454. https://doi.org/10.3389/fnins.2017.00454
Sen-Bhattacharya, Basabdatta, et al. "A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine." Frontiers in neuroscience vol. 11 (2017): 454. doi: https://doi.org/10.3389/fnins.2017.00454
Sen-Bhattacharya B, Serrano-Gotarredona T, Balassa L, Bhattacharya A, Stokes AB, Rowley A, Sugiarto I, Furber S. A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine. Front Neurosci. 2017 Aug 09;11:454. doi: 10.3389/fnins.2017.00454. eCollection 2017. PMID: 28848380; PMCID: PMC5552764.
Copy Download .nbib Format: NLM
Share it on

Front Neurosci. 2017 Aug 09;11:454. doi: 10.3389/fnins.2017.00454. eCollection 2017.

A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine.

Frontiers in neuroscience

Basabdatta Sen-Bhattacharya, Teresa Serrano-Gotarredona, Lorinc Balassa, Akash Bhattacharya, Alan B Stokes, Andrew Rowley, Indar Sugiarto, Steve Furber

Affiliations

  1. Advanced Processor Technologies Group, School of Computer Science, University of ManchesterManchester, United Kingdom.
  2. Instituto de Microelectronica de SevillaSevilla, Spain.
  3. Imperial College LondonLondon, United Kingdom.

PMID: 28848380 PMCID: PMC5552764 DOI: 10.3389/fnins.2017.00454

Abstract

We present a spiking neural network model of the thalamic Lateral Geniculate Nucleus (LGN) developed on SpiNNaker, which is a state-of-the-art digital neuromorphic hardware built with very-low-power ARM processors. The parallel, event-based data processing in SpiNNaker makes it viable for building massively parallel neuro-computational frameworks. The LGN model has 140 neurons representing a "basic building block" for larger modular architectures. The motivation of this work is to simulate biologically plausible LGN dynamics on SpiNNaker. Synaptic layout of the model is consistent with biology. The model response is validated with existing literature reporting entrainment in steady state visually evoked potentials (SSVEP)-brain oscillations corresponding to periodic visual stimuli recorded via electroencephalography (EEG). Periodic stimulus to the model is provided by: a synthetic spike-train with inter-spike-intervals in the range 10-50 Hz at a resolution of 1 Hz; and spike-train output from a state-of-the-art electronic retina subjected to a light emitting diode flashing at 10, 20, and 40 Hz, simulating real-world visual stimulus to the model. The resolution of simulation is 0.1 ms to ensure solution accuracy for the underlying differential equations defining Izhikevichs neuron model. Under this constraint, 1 s of model simulation time is executed in 10 s real time on SpiNNaker; this is because simulations on SpiNNaker work in real time for time-steps

Keywords: LGN interneurons; SpiNNaker machine; electronic retina; entrainment; lateral geniculate nucleus; multi-node models; sPyNNaker; steady state visually evoked potentials

References

  1. Nat Rev Neurosci. 2006 Feb;7(2):153-60 - PubMed
  2. Cell Calcium. 2006 Aug;40(2):175-90 - PubMed
  3. J Neurophysiol. 1999 Feb;81(2):702-11 - PubMed
  4. J Neurosci. 2008 Jan 16;28(3):660-71 - PubMed
  5. Prog Neurobiol. 2010 Apr;90(4):418-38 - PubMed
  6. J Neurophysiol. 1996 Dec;76(6):4152-68 - PubMed
  7. Front Neurosci. 2012 Nov 19;6:169 - PubMed
  8. J Neurosci. 2003 Aug 20;23(20):7642-6 - PubMed
  9. J Neuroeng Rehabil. 2011 Jul 14;8:39 - PubMed
  10. J Neurosci Methods. 2012 Sep 15;210(1):110-8 - PubMed
  11. J Vis. 2015;15(6):4 - PubMed
  12. Trends Neurosci. 2004 Nov;27(11):670-5 - PubMed
  13. Curr Opin Neurobiol. 2014 Apr;25:1-6 - PubMed
  14. Trends Neurosci. 2016 Oct;39(10 ):680-693 - PubMed
  15. J Physiol. 2012 Jun 1;590(11):2571-5 - PubMed
  16. Front Neuroinform. 2009 Jan 27;2:11 - PubMed
  17. Proc Natl Acad Sci U S A. 2008 Mar 4;105(9):3593-8 - PubMed
  18. J Neural Eng. 2016 Oct;13(5):051001 - PubMed
  19. Network. 2002 Feb;13(1):131-56 - PubMed
  20. J Comp Neurol. 2000 Jan 24;416(4):509-20 - PubMed
  21. Exp Brain Res. 2001 Apr;137(3-4):346-53 - PubMed
  22. J Physiol. 1988 May;399:153-76 - PubMed
  23. PLoS Comput Biol. 2014 Aug 14;10(8):e1003787 - PubMed
  24. Front Comput Neurosci. 2016 Nov 16;10 :115 - PubMed
  25. Trends Neurosci. 2001 Feb;24(2):122-6 - PubMed
  26. Neural Netw. 2011 Aug;24(6):631-45 - PubMed
  27. J Physiol. 1995 Mar 15;483 ( Pt 3):641-63 - PubMed
  28. Front Neurosci. 2016 Nov 02;10 :496 - PubMed
  29. Front Neural Circuits. 2014 May 20;8:46 - PubMed
  30. Nat Med. 1999 Dec;5(12):1349-51 - PubMed
  31. Front Neuroanat. 2016 Apr 07;10:37 - PubMed
  32. Annu Rev Neurosci. 2015 Jul 8;38:309-29 - PubMed
  33. Neural Comput. 2015 Oct;27(10):2148-82 - PubMed
  34. Front Hum Neurosci. 2016 Feb 03;10:10 - PubMed
  35. IEEE Trans Neural Netw. 2003;14(6):1569-72 - PubMed
  36. IEEE Trans Neural Netw. 2004 Sep;15(5):1063-70 - PubMed
  37. Science. 1999 Jan 22;283(5401):541-3 - PubMed
  38. Front Comput Neurosci. 2016 Dec 27;10 :129 - PubMed
  39. Electroencephalogr Clin Neurophysiol. 1973 Dec;35(6):627-39 - PubMed
  40. J Neurophysiol. 2001 Mar;85(3):1107-18 - PubMed

Publication Types