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Spatio-temporal Spike Pattern Classification in Neuromorphic Systems

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Biomimetic and Biohybrid Systems (Living Machines 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8064))

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Abstract

Spike-based neuromorphic electronic architectures offer an attractive solution for implementing compact efficient sensory-motor neural processing systems for robotic applications. Such systems typically comprise event-based sensors and multi-neuron chips that encode, transmit, and process signals using spikes. For robotic applications, the ability to sustain real-time interactions with the environment is an essential requirement. So these neuromorphic systems need to process sensory signals continuously and instantaneously, as the input data arrives, classify the spatio-temporal information contained in the data, and produce appropriate motor outputs in real-time. In this paper we evaluate the computational approaches that have been proposed for classifying spatio-temporal sequences of spike-trains, derive the main principles and the key components that are required to build a neuromorphic system that works in robotic application scenarios, with the constraints imposed by the biologically realistic hardware implementation, and present possible system-level solutions.

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References

  1. Maass, W., Sontag, E.: Neural systems as nonlinear filters. Neural Computation 12(8), 1743–1772 (2000)

    Article  Google Scholar 

  2. Belatreche, A., Maguire, L.P., McGinnity, M.: Advances in design and application of spiking neural networks. Soft Computing 11(3), 239–248 (2006)

    Article  MathSciNet  Google Scholar 

  3. Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J., Diesmann, M., Morrison, A., Goodman, P.H.J.F., Zirpe, M., Natschläger, T., Pecevski, D., Ermentrout, B., Djurfeldt, M., Lansner, A., Rochel, O., Vieville, T., Muller, E., Davison, A., El Boustani, S., Destexhe, A.: Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience 23(3), 349–398 (2007)

    Article  MathSciNet  Google Scholar 

  4. Liu, S.C., Delbruck, T.: Neuromorphic sensory systems. Current Opinion in Neurobiology 20(3), 288–295 (2010)

    Article  Google Scholar 

  5. Indiveri, G., Linares-Barranco, B., Hamilton, T., van Schaik, A., Etienne-Cummings, R., Delbruck, T., Liu, S.C., Dudek, P., Häfliger, P., Renaud, S., Schemmel, J., Cauwenberghs, G., Arthur, J., Hynna, K., Folowosele, F., Saighi, S., Serrano-Gotarredona, T., Wijekoon, J., Wang, Y., Boahen, K.: Neuromorphic silicon neuron circuits. Frontiers in Neuroscience 5, 1–23 (2011)

    Google Scholar 

  6. Choudhary, S., et al.: Silicon neurons that compute. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012, Part I. LNCS, vol. 7552, pp. 121–128. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  7. Yu, T., Park, J., Joshi, S., Maier, C., Cauwenberghs, G.: 65k-neuron integrate-and-fire array transceiver with address-event reconfigurable synaptic routing. In: Biomedical Circuits and Systems Conference (BioCAS), pp. 21–24. IEEE (November 2012)

    Google Scholar 

  8. Carr, C.E., Konishi, M.: Axonal delay lines for time measurement in the owl’s brainstem. Proceedings of the National Academy of Sciences 85(21), 8311–8315 (1988)

    Article  Google Scholar 

  9. Carr, C.E., Konishi, M.: A circuit for detection of interaural time differences in the brain stem of the barn owl. The Journal of Neuroscience 10(10), 3227–3246 (1990)

    Google Scholar 

  10. Pfeiffer, M., Hartbauer, M., Lang, A.B., Maass, W., Römer, H.: Probing real sensory worlds of receivers with unsupervised clustering. PloS One 7(6), e37354 (2012)

    Google Scholar 

  11. Johansson, R., Birznieks, I.: First spikes in ensembles of human tactile afferents code complex spatial fingertip events. Nature Neuroscience 7(2), 170–177 (2004)

    Article  Google Scholar 

  12. Singer, W.: Time as coding space? Current Opinion in Neurobiology 9(2), 189–194 (1999)

    Article  Google Scholar 

  13. O’Keefe, J., Burgess, N.: Geometric determinants of the place fields of hippocampal neurons. Nature 381(6581), 425–428 (1996)

    Article  Google Scholar 

  14. Stiefel, K.M., Tapson, J., van Schaik, A.: Temporal order detection and coding in nervous systems. Neural Computation 25(2), 510–531 (2013)

    Article  Google Scholar 

  15. Dhoble, K., Nuntalid, N., Indiveri, G., Kasabov, N.: Online spatio-temporal pattern recognition with evolving spiking neural networks utilising address event representation, rank order, and temporal spike learning. In: International Joint Conference on Neural Networks, IJCNN 2012, pp. 554–560. IEEE (2012)

    Google Scholar 

  16. Nessler, B., Pfeiffer, M., Maass, W.: Stdp enables spiking neurons to detect hidden causes of their inputs. In: Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 22, pp. 1357–1365 (2009)

    Google Scholar 

  17. Masquelier, T., Guyonneau, R., Thorpe, S.J.: Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PLoS One 3(1), e1377 (2008)

    Google Scholar 

  18. Gütig, R., Sompolinsky, H.: The tempotron: a neuron that learns spike timing–based decisions. Nature Neuroscience 9, 420–428 (2006)

    Article  Google Scholar 

  19. Thorpe, S., Delorme, A., Van Rullen, R., et al.: Spike-based strategies for rapid processing. Neural Networks 14(6-7), 715–725 (2001)

    Article  Google Scholar 

  20. Legenstein, R., Näger, C., Maass, W.: What can a neuron learn with spike-timing-dependent plasticity? Neural Computation 17(11), 2337–2382 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  21. Kempter, R., Gerstner, W., Van Hemmen, J.L.: How the threshold of a neuron determines its capacity for coincidence detection. Biosystems 48(1), 105–112 (1998)

    Article  Google Scholar 

  22. Gerstner, W., Kistler, W.: Spiking Neuron Models. In: Single Neurons, Populations, Plasticity. Cambridge University Press (2002)

    Google Scholar 

  23. Masquelier, T., Guyonneau, R., Thorpe, S.J.: Competitive stdp-based spike pattern learning. Neural Computation 21(5), 1259–1276 (2009)

    Article  MATH  Google Scholar 

  24. Lichtsteiner, P., Posch, C., Delbruck, T.: A 128×128 120dB 30mW asynchronous vision sensor that responds to relative intensity change. In: 2006 IEEE ISSCC Digest of Technical Papers, pp. 508–509. IEEE (February 2006)

    Google Scholar 

  25. Nessler, B., Pfeiffer, M., Maass, W.: Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity. PLoS Computational Biology (2013)

    Google Scholar 

  26. Gütig, R., Sompolinsky, H.: Time-warp-invariant neuronal processing. PLoS Biology 7(7), e1000141 (2009)

    Google Scholar 

  27. Koch, C., Poggio, T., Torre, V.: Nonlinear interactions in a dendritic tree: Localization, timing, and role in information processing. Proceedings of the National Academy of Sciences of the USA 80, 2799–2802 (1983)

    Article  Google Scholar 

  28. Wang, Y., Liu, S.C.: Multilayer processing of spatiotemporal spike patterns in a neuron with active dendrites. Neural Computation 8, 2086–2112 (2010)

    Article  Google Scholar 

  29. Arthur, J., Boahen, K.: Recurrently connected silicon neurons with active dendrites for one-shot learning. In: IEEE International Joint Conference on Neural Networks, vol. 3, pp. 1699–1704 (July 2004)

    Google Scholar 

  30. Ramakrishnan, S., Wunderlich, R., Hasler, P.: Neuron array with plastic synapses and programmable dendrites. In: Biomedical Circuits and Systems Conference (BioCAS), pp. 400–403. IEEE (November 2012)

    Google Scholar 

  31. Rasche, C., Douglas, R.: Forward- and backpropagation in a silicon dendrite. IEEE Transactions on Neural Networks 12, 386–393 (2001)

    Article  Google Scholar 

  32. Mill, R., Sheik, S., Indiveri, G., Denham, S.: A model of stimulus-specific adaptation in neuromorphic aVLSI. In: Biomedical Circuits and Systems Conference (BioCAS), pp. 266–269. IEEE (2010)

    Google Scholar 

  33. Buonomano, D.: Decoding temporal information: A model based on short-term synaptic plasticity. The Journal of Neuroscience 20, 1129–1141 (2000)

    Google Scholar 

  34. Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)

    Article  MATH  Google Scholar 

  35. Maass, W., Natschläger, T., Markram, H.: Fading memory and kernel properties of generic cortical microcircuit models. Journal of Physiology – Paris 98(4-6), 315–330 (2004)

    Article  Google Scholar 

  36. Sheik, S., Coath, M., Indiveri, G., Denham, S., Wennekers, T., Chicca, E.: Emergent auditory feature tuning in a real-time neuromorphic VLSI system. Frontiers in Neuroscience 6(17) (2012)

    Google Scholar 

  37. Coath, M., Mill, R., Denham, S.L., Wennekers, T.: Emergent Feature Sensitivity in a Model of the Auditory Thalamocortical System. Advances in Experimental Medicine and Biology, vol. 718, pp. 7–17. Springer, New York (2011)

    Google Scholar 

  38. Izhikevich, E.M.: Polychronization: Computation with spikes. Neural Computation 18(2), 245–282 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  39. Reichardt, W.: Autocorrelation, a principle for the evaluation of sensory information by the central nervous system. Sensory Communication, 303–317 (1961)

    Google Scholar 

  40. Wyss, R., König, P., Verschure, P.F.: Invariant representations of visual patterns in a temporal population code. Proceedings of the National Academy of Sciences 100(1), 324–329 (2003)

    Article  Google Scholar 

  41. Jeffress, L.A.: A place theory of sound localization. J. Comp. Physiol. Psychol. 41(1), 35–39 (1948)

    Article  Google Scholar 

  42. Liu, S.C., Kramer, J., Indiveri, G., Delbruck, T., Douglas, R.: Analog VLSI:Circuits and Principles. MIT Press (2002)

    Google Scholar 

  43. Bartolozzi, C., Indiveri, G.: Synaptic dynamics in analog VLSI. Neural Computation 19(10), 2581–2603 (2007)

    Article  MATH  Google Scholar 

  44. Bartolozzi, C., Mitra, S., Indiveri, G.: An ultra low power current–mode filter for neuromorphic systems and biomedical signal processing. In: Biomedical Circuits and Systems Conference (BioCAS), pp. 130–133. IEEE (2006)

    Google Scholar 

  45. Drakakis, E., Payne, A., Toumazou, C.: “Log-domain state-space”: A systematic transistor-level approach for log-domain filtering. IEEE Transactions on Circuits and Systems II 46(3), 290–305 (1999)

    Article  Google Scholar 

  46. Frey, D.: Log-domain filtering: An approach to current-mode filtering. IEE Proceedings G: Circuits, Devices and Systems 140(6), 406–416 (1993)

    Article  Google Scholar 

  47. Markram, H., Tsodyks, M.: Redistribution of synaptic efficacy between neocortical pyramidal neurons. Nature 382, 807–810 (1996)

    Article  Google Scholar 

  48. Rasche, C., Hahnloser, R.: Silicon synaptic depression. Biological Cybernetics 84(1), 57–62 (2001)

    Article  Google Scholar 

  49. Boegerhausen, M., Suter, P., Liu, S.C.: Modeling short-term synaptic depression in silicon. Neural Computation 15(2), 331–348 (2003)

    Article  MATH  Google Scholar 

  50. Varela, J., Sen, K., Gibson, J., Fost, J., Abbott, L., Nelson, S.: A quantitative description of short–term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex. The Journal of Neuroscience 17, 7926–7940 (1997)

    Google Scholar 

  51. Mill, R., Sheik, S., Indiveri, G., Denham, S.: A model of stimulus-specific adaptation in neuromorphic analog VLSI. Transactions on Biomedical Circuits and Systems 5(5), 413–419 (2011)

    Article  Google Scholar 

  52. Basu, A., Ramakrishnan, S., Petre, C., Koziol, S., Brink, S., Hasler, P.: Neural dynamics in reconfigurable silicon. IEEE Transactions on Biomedical Circuits and Systems 4(5), 311–319 (2010)

    Article  Google Scholar 

  53. Sheik, S., Chicca, E., Indiveri, G.: Exploiting device mismatch in neuromorphic VLSI systems to implement axonal delays. In: International Joint Conference on Neural Networks, IJCNN 2012, pp. 1940–1945. IEEE (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Insitute of Neuroinformatics, University of Zurich and ETH Zurich, Wintherthurerstrasse 190, 8057, Zurich, Switzerland

    Sadique Sheik, Michael Pfeiffer, Fabio Stefanini & Giacomo Indiveri

Authors
  1. Sadique Sheik
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  2. Michael Pfeiffer
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  3. Fabio Stefanini
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  4. Giacomo Indiveri
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Editor information

Editors and Affiliations

  1. University of Sheffield, UK

    Nathan F. Lepora  & Tony J. Prescott  & 

  2. University of Pompeau Fabra, Barcelona, Spain

    Anna Mura

  3. Imperial College, London, UK

    Holger G. Krapp

  4. Catalan Institution for Research and Advanced Studies, University of Pompeau Fabra, Barcelona, Spain

    Paul F. M. J. Verschure

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Sheik, S., Pfeiffer, M., Stefanini, F., Indiveri, G. (2013). Spatio-temporal Spike Pattern Classification in Neuromorphic Systems. In: Lepora, N.F., Mura, A., Krapp, H.G., Verschure, P.F.M.J., Prescott, T.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2013. Lecture Notes in Computer Science(), vol 8064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39802-5_23

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  • DOI: https://doi.org/10.1007/978-3-642-39802-5_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39801-8

  • Online ISBN: 978-3-642-39802-5

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