Advertisement

Spike-Timing-Dependent-Plasticity with Memristors

  • T. Serrano-Gotarredona
  • T. Masquelier
  • B. Linares-Barranco

Abstract

(This chapter is reprints material from Zamarreño-Ramos et al. in Front. Neurosci. 5:26, 2011 and Serrano-Gotarredona et al. in Front. Neurosci. 7:02, 2013, with permission.) Here we present a very exciting overlap between emergent nano technology and neuroscience, which has been discovered by neuromorphic engineers. Specifically, we are linking one type of memristor nano technology devices to the biological synaptic update rule known as Spike-Time-Dependent-Plasticity found in real biological synapses. Understanding this link allows neuromorphic engineers to develop circuit architectures that use this type of memristors to artificially emulate parts of the visual cortex. We focus on the type of memristors referred to as voltage or flux driven memristors and focus our discussions on behavioral macro models for such devices. The implementations result in fully asynchronous architectures with neurons sending their action potentials not only forwards but also backwards. One critical aspect is to use neurons that generate spikes of specific shapes. We will see how by changing the shapes of the neuron action potential spikes we can tune and manipulate the STDP learning rules for both excitatory and inhibitory synapses. We will see how neurons and memristors can be interconnected to achieve large scale spiking learning systems, that follow a type of multiplicative STDP learning rule. We will briefly extend the architectures to use three-terminal transistors with similar memristive behavior. We will illustrate how a V1 visual cortex layer can be assembled and how it is capable of learning to extract orientations from visual data coming from a real artificial CMOS spiking retina observing real life scenes. Finally, we will discuss limitations of currently available memristors. The results presented are based on behavioral simulations and do not take into account non-idealities of devices and interconnects. The aim here is to present, in a tutorial manner, an initial framework for the possible development of fully asynchronous STDP learning neuromorphic architectures exploiting two or three terminal memristive type devices. (A Supplemental Material compressed zip file containing all files used for the simulations can be downloaded from http://www.frontiersin.org/neuromorphic_engineering/10.3389/fnins.2011.00026/abstract.)

Keywords

Synaptic Strength Memristor Device Neural Spike Effective Cross Section Area Action Potential Shape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Zamarreño-Ramos, C., Camuñas-Mesa, L.A., Perez-Carrasco, J.A., Masquelier, T., Serrano-Gotarredona, T., Linares-Barranco, B.: On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex. Front. Neuromorphic Eng., Front. Neurosci. 5, 26 (2011). Available from www.frontiersin.org/neuromorphic_engineering/10.3389/fnins.2011.00026/abstract Google Scholar
  2. 2.
    Serrano-Gotarredona, T., Masquelier, T., Prodromakis, T., Indiveri, G., Linares-Barranco, B.: STDP and STDP variations with memristors for spiking neuromorphic learning systems. Front. Neuromorphic Eng., Front. Neurosci. 7, 02 (2013). Available from www.frontiersin.org/Neuroscience/10.3389/fnins.2013.00002/abstract Google Scholar
  3. 3.
  4. 4.
    Mead, C.: Analog VLSI and Neural Systems. Addison-Wesley, Reading (1989) zbMATHGoogle Scholar
  5. 5.
    Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The missing memristor found. Nature 453(1), 80–83 (2008) Google Scholar
  6. 6.
    Borghetti, J., Li, Z., Straznicky, J., Li, X., Ohlberg, D.A.A., Wu, W., Stewart, D.R., Williams, R.S.: A hybrid nanomemristor/transistor logic circuit capable of self-programming. Proc. Natl. Acad. Sci. USA 106(6), 1699–1703 (2009) Google Scholar
  7. 7.
    Jo, S.H., Chang, T., Ebong, I., Bhadviya, B.B., Mazumder, P., Lu, W.: Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10(4), 1297–1301 (2010) Google Scholar
  8. 8.
    Jo, S.H., Kim, K.-H., Lu, W.: High-density crossbar arrays based on a Si memristive system. Nano Lett. 9(2), 870–874 (2009) Google Scholar
  9. 9.
    Chua, L.O.: Memristor—the missing circuit element. IEEE Trans. Circuit Theory 18, 507–519 (1971) Google Scholar
  10. 10.
    Chua, L.O., Kang, S.M.: Memristive devices and systems. Proc. IEEE 64(2), 209–223 (1976) MathSciNetGoogle Scholar
  11. 11.
    Chua, L.O., Desoer, C.A., Kuh, E.S.: Linear and Nonlinear Circuits. McGraw-Hill, New York (1987) zbMATHGoogle Scholar
  12. 12.
    Gerstner, W., Ritz, R., Hemmen, J.L.: Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns. Biol. Cybern. 69, 503–515 (1993) zbMATHGoogle Scholar
  13. 13.
    Gerstner, W., Kempter, R., van Hemmen, J.L., Wagner, H.: A neuronal learning rule for sub-millisecond temporal coding. Lett. Nat. 383, 76–78 (1996) Google Scholar
  14. 14.
    Sjöström, J., Gerstner, W.: Spike-timing dependent plasticity. In: Scholarpedia, the Peer-Reviewed Open-Access Encyclopedia, vol. 5(2), p. 1362 (2010). Available from http://www.scholarpedia.org/article/STDP Google Scholar
  15. 15.
    Rao, R.P.N., Sejnowski, T.J.: Spike-time-dependent Hebbian plasticity as temporal difference learning. Neural Comput. 13, 2221–2237 (2001) zbMATHGoogle Scholar
  16. 16.
    Porr, B., Wörgötter, F.: How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model. Neural Comput. 16, 595–625 (2004) zbMATHGoogle Scholar
  17. 17.
    Delorme, A., Perrinet, L., Thorpe, S.J.: Networks of integrate-and-fire neurons using rank order coding B: spike timing dependent plasticity and emergence of orientation selectivity. Neurocomputing 38–40, 539–545 (2001) Google Scholar
  18. 18.
    Guyonneau, R., VanRullen, R., Thorpe, S.J.: Temporal codes and sparse representations: a key to understanding rapid processing in the visual system. J. Physiol. Paris 98(4–6), 487–497 (2004) Google Scholar
  19. 19.
    Masquelier, T., Thorpe, S.J.: Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Comput. Biol. 3(2), e31 (2007) Google Scholar
  20. 20.
    Weidenbacher, U., Neumann, H.: Unsupervised learning of head pose through spike-timing dependent plasticity. In: Perception in Multimodal Dialogue Systems. Lecture Notes in Computer Science, vol. 5078, pp. 123–131. Springer, Berlin (2008) Google Scholar
  21. 21.
    Masquelier, T., Thorpe, S.J.: Learning to recognize objects using waves of spikes and spike timing-dependent plasticity. In: Proc. of the 2010 IEEE Int. Joint Conf. on Neural Networks (2010). doi: 10.1109/IJCNN.2010.5596934 Google Scholar
  22. 22.
    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
  23. 23.
    Masquelier, T., Guyonneau, R., Thorpe, S.J.: Competitive STDP-based spike pattern learning. Neural Comput. 21(5), 1259–1276 (2009). doi: 10.1162/neco.2008.06-08-804 zbMATHGoogle Scholar
  24. 24.
    Masquelier, T., Hugues, E., Deco, G., Thorpe, S.J.: Oscillations, phase-of-firing coding and spike timing-dependent plasticity: an efficient learning scheme. J. Neurosci. 29(43), 13484–13493 (2009) Google Scholar
  25. 25.
    Young, J.M.: Cortical reorganization consistent with spike timing-but not correlation-dependent plasticity. Nat. Neurosci. 10(7), 887–895 (2007) Google Scholar
  26. 26.
    Finelli, L.A., Haney, S., Bazhenov, M., Stopfer, M., Sejnowski, T.J.: Synaptic learning rules and sparse coding in a model sensory system. PLoS Comput. Biol. 4(4), e1000062 (2008) MathSciNetGoogle Scholar
  27. 27.
    Hebb, D.O.: The Organization of Behavior, a Neuropsychological Study. Wiley, New York (1949) Google Scholar
  28. 28.
    Snider, G.S.: Self-organized computation with unreliable, memristive nanodevices. Nanotechnology 18, 365202 (2007) Google Scholar
  29. 29.
    Snider, G.S.: Spike-timing-dependent learning in memristive nanodevices. In: IEEE Int. Symp. Nano Architectures, pp. 85–92 (2008) Google Scholar
  30. 30.
    Linares-Barranco, B., Serrano-Gotarredona, T.: Memristance can explain spike-time-dependent-plasticity in neural synapses. Available from Nature Precedings http://hdl.handle.net/10101/npre.2009.3010.1, March 2009
  31. 31.
    Markram, H., Lübke, J., Frotscher, M., Sakmann, B.: Regulation of synaptic efficacy by coincidence of postsynaptic APS and EPSPS. Science 275(5297), 213–215 (1997) Google Scholar
  32. 32.
    Bi, G., Poo, M.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18(24), 10464–10472 (1998) Google Scholar
  33. 33.
    Bi, G., Poo, M.M.: Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu. Rev. Neurosci. 24, 139–166 (2001) Google Scholar
  34. 34.
    Zhang, L., Tao, H., Holt, C., Harris, W., Poo, M.: A critical window for cooperation and competition among developing retinotectal synapses. Nature 395(6697), 37–44 (1998) Google Scholar
  35. 35.
    Feldman, D.: Timing-based LTP and LTD at vertical inputs to layer II/III pyramidal cells in rat barrel cortex. Neuron 27(1), 45–56 (2000) Google Scholar
  36. 36.
    Mu, Y., Poo, M.M.: Spike timing-dependent LTP/LTD mediates visual experience-dependent plasticity in a developing retinotectal system. Neuron 50(1), 115–125 (2006) Google Scholar
  37. 37.
    Cassenaer, S., Laurent, G.: Hebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts. Nature 448(7154), 709–713 (2007) Google Scholar
  38. 38.
    Jacob, V., et al.: Spike-timing-dependent synaptic depression in the in vivo barrel cortex of the rat. J. Neurosci. 27(6), 1271–1284 (2007) Google Scholar
  39. 39.
    Rubin, J.E., Gerkin, R.C., Bi, G.-Q., Chow, C.C.: Calcium time course as a signal for spike-timing-dependent plasticity. J. Neurophysiol. 93, 2600–2613 (2005) Google Scholar
  40. 40.
    Lubenov, E.V., Siapas, A.G.: Decoupling through synchrony in neuronal circuits with propagation delays. Neuron 58, 118–131 (2008) Google Scholar
  41. 41.
    Querlioz, D., Bichler, O., Gamrat, C.: Simulation of a memristor-based spiking neural network immune to device variations. In: Proc. Int. Joint Conf. Neural Networks (IJCNN), pp. 1775–1781 (2011) Google Scholar
  42. 42.
    Bichler, O., Suri, M., Querlioz, D., Vuillaume, D., DeSalvo, B., Gamrat, C.: Visual pattern extraction using energy-efficient “2-PCM synapse” neuromorphic architecture. IEEE Trans. Electron Devices 59(8), 2206–2214 (2012) Google Scholar
  43. 43.
    Bichler, O., Querlioz, D., Thorpe, S.J., Bourgoin, J.P., Gamrat, C.: Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity. Neural Netw. 32, 339–348 (2012) Google Scholar
  44. 44.
    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.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems, pp. 1357–1365. MIT Press, Cambridge (2010) Google Scholar
  45. 45.
    Abbott, L.F., Nelson, S.B.: Synaptic plasticity: taming the beast. Nat. Neurosci. 3, 1178–1183 (2000) Google Scholar
  46. 46.
    van Rossum, M.C.W., Bi, G.Q., Turrigiano, G.G.: Stable Hebbian learning from spike timing-dependent plasticity. J. Neurosci. 20(23), 8812–8821 (2000) Google Scholar
  47. 47.
    Rubin, J., Lee, D.D., Sompolinsky, H.: Equilibrium properties of temporally asymmetric Hebbian plasticity. Phys. Rev. Lett. 86(2), 364–367 (2001) Google Scholar
  48. 48.
    Gütig, R., Aharonov, R., Rotter, S., Sompolinsky, H.: Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity. J. Neurosci. 23(9), 3697–3714 (2003) Google Scholar
  49. 49.
    Argall, F.: Switching phenomena in titanium oxide thin films. In: Solid-State Electronics, vol. 11, pp. 535–541. Pergamon, Elmsford (1968) Google Scholar
  50. 50.
    Swaroop, B., West, W.C., Martinez, G., Kozicki, M.N., Akers, L.A.: Programmable current mode Hebbian learning neural network using programmable metallization cell. In: Proc. of the IEEE Int. Symp. on Circ. and Syst. (ISCAS1998), pp. III.33–III.36 (1998) Google Scholar
  51. 51.
    Joglekar, Y.N., Wolf, S.J.: The elusive memristor: properties of basic electrical circuits. Eur. J. Phys. 30, 661–675 (2009) zbMATHGoogle Scholar
  52. 52.
    Biolek, Z., Biolek, D., Biolkova, V.: Spice model for memristor with nonlinear dopant drift. Radioengineering 18(2), 210–214 (2009) Google Scholar
  53. 53.
    Nian, Y., Strozier, J., Wu, N., Chen, X., Ignatiev, A.: Evidence for an oxygen diffusion model for the electric pulse induced resistance change effect in transition-metal oxides. Phys. Rev. Lett. 98(14), 146–403 (2007) Google Scholar
  54. 54.
    Yang, J.J., Pickett, M.D., Li, X., Ohlberg, D.A.A., Stewart, D.R., Williams, R.S.: Memristive switching mechanism for metal/oxide/metal nanodevices. Nat. Nanotechnol. 3(7), 429–433 (2008) Google Scholar
  55. 55.
    Hur, J., Lee, M.-J., Lee, C., Kim, Y.-B., Kim, C.J.: Modeling for bipolar resistive memory switching in transition-metal oxides. Phys. Rev. B 82(15) (2010) Google Scholar
  56. 56.
    Wuttig, M., Yamada, N.: Phase-change materials for rewriteable data storage. Nat. Mater. 6(11), 824–832 (2007) Google Scholar
  57. 57.
    Kwon, D.H., et al.: Atomic structure of conducting nanofilaments in TiO2 resistive switching memory. Nat. Nanotechnol. 5, 148–153 (2010) Google Scholar
  58. 58.
    Yang, Y., Gao, P., Gaba, S., Chang, T., Pan, X., Lu, W.: Observation of conducting filament growth in nanoscale resistive memories. Nat. Commun. 3, 732 (2012) Google Scholar
  59. 59.
    Shihong, M.W., Prodromakis, T., Salaoru, I., Toumazou, C.: Modelling of current percolation channels in emerging resistive switching elements. arXiv:1206.2746v1 [cond-mat.mes-hall]
  60. 60.
    Chanthbouala, A., Garcia, V., Cherifi, R.O., Bouzehouane, K., Fusil, S., Moya, X., Xavier, S., Yamada, H., Deranlot, C., Mathur, N.D., Bibes, M., Bartélémy, A., Grollier, J.: A ferroelectric memristor. Nat. Mater. 11, 860–864 (2012) Google Scholar
  61. 61.
    Indiveri, G., et al.: Neuromorphic silicon neuron circuits. Inaugural issue of Frontiers in Neuromorphic Engineering. http://www.frontiersin.org/neuromorphic_engineering
  62. 62.
    Maass, W.: Private communication Google Scholar
  63. 63.
    Sivilotti, M.: Wiring considerations in analog VLSI systems with application to field-programmable networks. PhD, Computation and Neural Systems, Caltech, Pasadena California (1991) Google Scholar
  64. 64.
    Mahowald, M.A.: VLSI analogs of neuronal visual processing: a synthesis of form and function. PhD, Computation and Neural Systems, Caltech, Pasadena, California (1992) Google Scholar
  65. 65.
    Lazzaro, J., Wawrzynek, J., Mahowald, M., Silvilotti, M., Gillespie, D.: Silicon auditory processors as computer peripherals. IEEE Trans. Neural Netw. 4, 523–528 (1993) Google Scholar
  66. 66.
    Cauwenberghs, G., Kumar, N., Himmelbauer, W., Andreou, A.G.: An analog VLSI chip with asynchronous interface for auditory feature extraction. IEEE Trans. Circuits Syst., Part II 45, 600–606 (1998) Google Scholar
  67. 67.
    Boahen, K.: Retinomorphic chips that see quadruple images. In: Proc. Int. Conf. Microelectronics for Neural, Fuzzy and Bio-Inspired Systems (Microneuro99), Granada, Spain, pp. 12–20 (1999) Google Scholar
  68. 68.
    Boahen, K.: A retinomorphic chip with parallel pathways: encoding INCREASING, ON, DECREASING, and OFF visual signals. Int. J. Analog Integr. Circuits Signal Process. 30, 121–135 (2002) Google Scholar
  69. 69.
    Martin, A.J., Nyström, M.: Asynchronous techniques for system-on-chip design. Proc. IEEE 94(6), 1089–1120 (2006) Google Scholar
  70. 70.
    Sparsø, J., Furber, S.B.: Principles of Asynchronous Circuit Design: A Systems Perspective. Kluwer Academic, Dordrecht (2001) Google Scholar
  71. 71.
    Mortara, A., Vittoz, E.A., Venier, P.: A communication scheme for analog VLSI perceptive systems. IEEE J. Solid-State Circuits 30(6), 660–669 (1995) Google Scholar
  72. 72.
    Boahen, K.: Retinomorphic vision systems. In: Microneuro’96: Fifth Int. Conf. on Neural Networks and Fuzzy Systems, Laousanne, Switzerland, February 1996 Google Scholar
  73. 73.
    Boahen, K.: Point-to-point connectivity between neuromorphic chips using address events. IEEE Trans. Circuits Syst., Part II 47(5), 416–434 (2000) zbMATHGoogle Scholar
  74. 74.
    Posch, C., et al.: A QVGA 143 dB dynamic range asynchronous address-event PWM dynamic image sensor with lossless pixel-level video compression. In: ISSCC Dig. of Tech. Papers, pp. 400–401 (2010) Google Scholar
  75. 75.
    Culurciello, E., Etienne-Cummings, R., Boahen, K.A.: A biomorphic digital image sensor. IEEE J. Solid-State Circuits 38, 281–294 (2003) Google Scholar
  76. 76.
    Ruedi, P.F., et al.: A 128×128, pixel 120-dB dynamic-range vision-sensor chip for image contrast and orientation extraction. IEEE J. Solid-State Circuits 38, 2325–2333 (2003) Google Scholar
  77. 77.
    Chen, S., Bermak, A.: Arbitrated time-to-first spike CMOS image sensor with on-chip histogram equalization. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 15(3), 346–357 (2007) Google Scholar
  78. 78.
    Azadmehr, M., Abrahamsen, J., Häfliger, P.: A foveated AER imager chip. In: Proc. of the IEEE Int. Symp. on Circ. and Syst. (ISCAS2005), Kobe, Japan, pp. 2751–2754 (2005) Google Scholar
  79. 79.
    Massari, N., et al.: A 100 uW 64×128-pixel contrast-based asynchronous binary vision sensor for wireless sensor networks. In: IEEE ISSCC Dig. of Tech. Papers, pp. 588–638 (2008) Google Scholar
  80. 80.
    Ruedi, P.F., et al.: An SoC combining a 132 dB QVGA pixel array and a 32 b DSP/MCU processor for vision applications. In: IEEE ISSCC Dig. of Tech. Papers, pp. 46–47, 47a (2009) Google Scholar
  81. 81.
    Costas-Santos, J., et al.: A contrast retina with on-chip calibration for neuromorphic spike-based AER vision systems. IEEE Trans. Circuits Syst. I, Regul. Pap. 54(7), 1444–1458 (2007) Google Scholar
  82. 82.
    Leñero-Bardallo, J.A., Serrano-Gotarredona, T., Linares-Barranco, B.: A five-decade dynamic-range ambient-light-independent calibrated signed-spatial-contrast AER retina with 0.1 ms latency and optional time-to-first-spike mode. IEEE Trans. Circuits Syst., Part I 57(10), 2632–2643 (2010) Google Scholar
  83. 83.
    Mallik, U., et al.: Temporal change threshold detection imager. In: IEEE ISSCC Dig. of Tech. Papers, pp. 362–363 (2005) Google Scholar
  84. 84.
    Posch, C., et al.: A dual-line optical transient sensor with on-chip precision time-stamp generation. In: IEEE ISSCC Dig. of Tech. Papers, pp. 500–618 (2007) Google Scholar
  85. 85.
    Lichtsteiner, P., Posch, C., Delbruck, T.: A 128×128 120 dB 30 mW asynchronous vision sensor that responds to relative intensity change. IEEE J. Solid-State Circuits 43(2), 566–576 (2008) Google Scholar
  86. 86.
    Leñero-Bardallo, J.A., Serrano-Gotarredona, T., Linares-Barranco, B.: A 3.6 μs latency frame-free event-driven dynamic vision sensor. IEEE J. Solid-State Circuits (2010) Google Scholar
  87. 87.
    Zaghloul, K.A., Boahen, K.: Optic nerve signals in a neuromorphic chip: part 1. IEEE Trans. Biomed. Eng. 51, 657–666 (2004) Google Scholar
  88. 88.
    Zaghloul, K.A., Boahen, K.: Optic nerve signals in a neuromorphic chip: part 2. IEEE Trans. Biomed. Eng. 51, 667–675 (2004) Google Scholar
  89. 89.
    Sarpeshkar, R., et al.: An analog bionic ear processor with zero-crossing detection. In: ISSCC Dig. of Tech. Papers, pp. 78–79 (2005) Google Scholar
  90. 90.
    Wen, B., Boahen, K.: A 360-channel speech preprocessor that emulates the cochlear amplifier. In: ISSCC Dig. of Tech. Papers, pp. 556–557 (2006) Google Scholar
  91. 91.
    Chan, V., Liu, S.-C., van Schaik, A.: AER EAR: a matched silicon cochlea pair with address event representation interface. IEEE Trans. Circuits Syst., Part I 54, 48–59 (2007) Google Scholar
  92. 92.
    Chicca, E., Whatley, A.M., Lichtsteiner, P., Dante, V., Delbruck, T., Del Giudice, P., Douglas, R.J., Indiveri, G.: A multichip pulse-based neuromorphic infrastructure and its application to a model of orientation selectivity. IEEE Trans. Circuits Syst., Part I 54(5), 981–993 (2007) Google Scholar
  93. 93.
    Oster, M., Wang, Y., Douglas, R., Liu, S.-C.: Quantification of a spike-based winner-take-all VLSI network. IEEE Trans. Circuits Syst., Part 1 55(10), 3160–3169 (2008) MathSciNetGoogle Scholar
  94. 94.
    Teixeira, T., Andreou, A.G., Culurciello, E.: Event-based imaging with active illumination in sensor networks. In: Proc. of the IEEE Int. Symp. on Circ. and Syst. (ISCAS2005), Kobe, Japan, pp. 644–647 (2005) Google Scholar
  95. 95.
    Vernier, P., Mortara, A., Arreguit, X., Vittoz, E.A.: An integrated cortical layer for orientation enhancement. IEEE J. Solid-State Circuits 32(2), 177–186 (1997) Google Scholar
  96. 96.
    Choi, T.Y.W., Merolla, P., Arthur, J., Boahen, K., Shi, B.E.: Neuromorphic implementation of orientation hypercolumns. IEEE Trans. Circuits Syst., Part I 52(6), 1049–1060 (2005) MathSciNetGoogle Scholar
  97. 97.
    Serrano-Gotarredona, T., Andreou, A.G., Linares-Barranco, B.: AER image filtering architecture for vision processing systems. IEEE Trans. Circuits Syst., Part I 46(9), 1064–1071 (1999) Google Scholar
  98. 98.
    Serrano-Gotarredona, R., et al.: A neuromorphic cortical-layer microchip for spike-based event processing vision systems. IEEE Trans. Circuits Syst., Part I 53(12), 2548–2566 (2006) Google Scholar
  99. 99.
    Serrano-Gotarredona, R., et al.: On real-time AER 2D convolutions hardware for neuromorphic spike based cortical processing. IEEE Trans. Neural Netw. 19(7), 1196–1219 (2008) Google Scholar
  100. 100.
    Camuñas-Mesa, L., Acosta-Jiménez, A., Zamarreño-Ramos, C., Serrano-Gotarredona, T., Linares-Barranco, B.: A 32×32 convolution processor chip for address event vision sensors with 155 ns event latency and 20 Meps throughput. IEEE Trans. Circuits Syst., Part-I 58(4), 777–790 (2011). doi: 10.1109/TCSI.2010.2078851 Google Scholar
  101. 101.
    Berge, H.K.O., Hafliger, P.: High-speed serial AER on FPGA. In: Proc. of the IEEE Int. Symp. on Circ. and Syst. (ISCAS2007), pp. 857–860 (2005) Google Scholar
  102. 102.
    Serrano-Gotarredona, R., et al.: CAVIAR: a 45 k neuron, 5 m synapse, 12 g connects/s AER hardware sensory-processing-learning-actuating system for high-speed visual object recognition and tracking. IEEE Trans. Neural Netw. 20(9), 1417–1438 (2009) Google Scholar
  103. 103.
    Pérez-Carrasco, J.A., Acha, B., Serrano, C., Camuñas-Mesa, L., Serrano-Gotarredona, T., Linares-Barranco, B.: Fast vision through frame-less event-based sensing and convolutional processing. application to texture recognition. IEEE Trans. Neural Netw. 21(4), 609–620 (2010) Google Scholar
  104. 104.
    Camuñas-Mesa, L., Pérez-Carrasco, J.A., Zamarreño-Ramos, c., Serrano-Gotarredona, T., Linares-Barranco, B.: On scalable spiking ConvNet hardware for cortex-like visual sensory processing systems. IEEE Int. Symp. Circuits Syst. Proc. 2010, 249–252 (2010) Google Scholar
  105. 105.
    Zamarreño-Ramos, C., Linares-Barranco, A., Serrano-Gotarredona, T., Linares-Barranco, B.: Multi-casting mesh AER: a scalable assembly approach for reconfigurable neuromorphic structured AER systems. Application to ConvNets. IEEE Trans. Biomed. Circuits Syst. 7(1), 82–102 (2013) Google Scholar
  106. 106.
    Hubel, D.H., Wiesel, T.N.: Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148, 574–591 (1959) Google Scholar
  107. 107.
    Strukov, D.B., Likharev, K.K.: CMOL FPGA: a reconfigurable architecture for hybrid digital circuits with two-terminal nanodevices. Nanotechnology 16, 888–900 (2005) Google Scholar
  108. 108.
    Delbruck, T.: Driving in Pasadena to the post office, from Vision Project jAER download area. Available at http://sourceforge.net/apps/trac/jaer/wiki/AER%20data
  109. 109.
    Goodman, D., Brette, R.: Brian: a simulator for spiking neural networks in python. Front. Neuroinform. 2, 5 (2008). doi: 10.3389/neuro.11.005.2008 Google Scholar
  110. 110.
    Gerstner, W.: Spiking neurons. In: Maass, W., Bishop, C.M. (eds.) Pulsed Neural Networks. MIT Press, Cambridge (1999). Chap. 1 Google Scholar
  111. 111.
    Green, J.E., Choi, J.W., Boukai, A., Bunimovich, Y., Johnston-Halperin, E., DeIonno, E., Luo, Y., Sheriff, B.A., Xu, K., Shin, Y.S., Tseng, H.-R., Stoddart, J.F., Heath, J.R.: A 160-kilobit molecular electronic memory patterned at 1011 bits per square centimetre. Nature 445, 414–417 (2007) Google Scholar
  112. 112.
    Jung, G.-Y., Johnston-Halperin, E., Wu, W., Yu, Z., Wang, S.-Y., Tong, W.M., Li, Z., Green, J.E., Sheriff, B.A., Boukai, A., Bunimovich, Y., Heath, J.R., Williams, R.S.: Circuit fabrication at 17 nm half-pitch by nanoimprint lithography. Nano Lett. 351–354 (2006) Google Scholar
  113. 113.
    Jeon, H.-J., Kim, K.H., Baek, Y.-K., Kim, D.W., Jung, H.-T.: New top-down approach for fabricating high-aspect-ratio complex nanostructures with 10 nm scale features. Nano Lett. 10(9), 3604–3610 (2010) Google Scholar
  114. 114.
    Izhikevich, E.M., Desai, N.S.: Relating STDP to BCM. Neural Comput. 15, 1511–1523 (2003) zbMATHGoogle Scholar
  115. 115.
    Watt, A.J., Desai, N.S.: Homeostatic plasticity and STDP: keeping a neuron’s cool in a fluctuating world. Front. Syn. Neurosci. 2, 5 (2010). doi: 10.3389/fnsyn.2010.00005 Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • T. Serrano-Gotarredona
    • 1
  • T. Masquelier
    • 2
  • B. Linares-Barranco
    • 1
  1. 1.Instituto de Microelectronica de Sevilla, Consejo Superior de Investigaciones CientificasUniversidad de SevillaSevillaSpain
  2. 2.Laboratory of Neurobiology and Adaptive ProcesssesUniversity Pierre et Marie CurieParisFrance

Personalised recommendations