Persuading Computers to Act More Like Brains

  • Heather Ames
  • Massimiliano Versace
  • Anatoli Gorchetchnikov
  • Benjamin Chandler
  • Gennady Livitz
  • Jasmin Léveillé
  • Ennio Mingolla
  • Dick Carter
  • Hisham Abdalla
  • Greg Snider
Chapter
First Online:
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 4)

Abstract

Convergent advances in neural modeling, neuroinformatics, neuromorphic engineering, materials science, and computer science will soon enable the development and manufacture of novel computer architectures, including those based on memristive technologies that seek to emulate biological brain structures. A new computational platform, Cog Ex Machina, is a flexible modeling tool that enables a variety of biological-scale neuromorphic algorithms to be implemented on heterogeneous processors, including both conventional and neuromorphic hardware. Cog Ex Machina is specifically designed to leverage the upcoming introduction of dense memristive memories close to computing cores. The MoNETA (Modular Neural Exploring Traveling Agent) model is comprised of such algorithms to generate complex behaviors based on functionalities that include perception, motivation, decision-making, and navigation. MoNETA is being developed with Cog Ex Machina to exploit new hardware devices and their capabilities as well as to demonstrate intelligent, autonomous behaviors in both virtual animats and robots. These innovations in hardware, software, and brain modeling will not only advance our understanding of how to build adaptive, simulated, or robotic agents, but will also create innovative technological applications with major impacts on general-purpose and high-performance computing.

Keywords

Hewlett Packard Chromatic Feature Biological Brain Information Information Silicon Neuron 
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.
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Notes

Acknowledgments

The work was supported in part by the Center of Excellence for Learning in Education, Science and Technology (CELEST), a National Science Foundation Science of Learning Center (NSF SBE-0354378 and NSF OMA-0835976). This work was also partially funded by the DARPA SyNAPSE program, contract HR0011-09-3-0001. The views, opinions, and/or findings contained in this chapter are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency, the Department of Defense, or the National Science Foundation.

References

  1. 1.
    Abrahamsen J, Hafliger P, Lande T (2004) A time domain winner-take-all network of integrate-and-fire neurons. IEEE Int Symp Circuits Syst 5:361–364Google Scholar
  2. 2.
    Afifi A, Ayatollahi A, Raissi F (2009) STDP implementation using memristive nano device in CMOS-Nano neuromorphic networks. IEICE Electron Express 6(3):148–153CrossRefGoogle Scholar
  3. 3.
    Ames H, Mingolla E, Sohail A, Chandler B, Gorchetchnikov A, Léveillé J, Livitz G, Versace M (2011) The Animat—New frontiers in whole-brain modeling. IEEE NEST (in press)Google Scholar
  4. 4.
    Ananthanarayanan R, Esser SK, Simon HD, Modha DS (2009) The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses. Proceedings of the conference on high performance computing networking, storage, and analysis, pp 1–12Google Scholar
  5. 5.
    Andreou AG, Meitzler RC, Strohben K, Boahen KA (1995) Analog VLSI neuromorphic image acquisition and pre-processing systems. Neural Net 8(7–8):1323–1347CrossRefGoogle Scholar
  6. 6.
    Argyrakis P, Hamilton A, Webb B, Zhang Y, Gonos T, Cheung, R (2007) Fabrication and characterization of a wind sensor for integration with neuron circuit. Microelectron Eng 84:1749–1753CrossRefGoogle Scholar
  7. 7.
    Arthur J, Boahen K (2006) Learning in silicon: timing is everything. In: Weiss Y, Scholkoph B, Platt J (eds) Advances in neural information processing systems, 18. MIT Press, Cambridge, pp 1–8Google Scholar
  8. 8.
    Bartolozzi C, Indiveri G (2007) Synpatic dynamics in analog VLSI. Neural Comput 19(10):2581–2603PubMedCrossRefGoogle Scholar
  9. 9.
    Basset DS, Greenfield DL, Meyer-Lindenberg A, Weinberg DR, Moore SW, Bullmore ET (2010) Efficient physical embedding of topologically complex information processing networks in brains and computer networks. PLoS Comput Biol e1000748Google Scholar
  10. 10.
    Bernabe L, Serrano-Gotarredona T (2009) Memristance can explain spike-time-dependent-plasticity in neural synapses. Nature Precedings. http://precedings.nature.com (hdl:10101/npre.2009.3010.1)Google Scholar
  11. 11.
    Bernabe K (1999) A throughput-on-demand address-event transmitter for neuromorphic chips. Advanced Research in VLSI, pp 72–86Google Scholar
  12. 12.
    Boahen K (2007) Synchrony in silicon: the gamma rhythm. IEEE Trans Neural Netw 18(6):1815–1825PubMedCrossRefGoogle Scholar
  13. 13.
    Boahen K, Andreou A (1992) A contrast sensitive silicon retina with reciprocal synapses. Adv Neural Inf Process Syst 4:764–772Google Scholar
  14. 14.
    Brockman WH (1979) A simple electronic neuron model incorporating both active and passive responses. IEEE Trans Biomed Eng BME-26:635–639PubMedCrossRefGoogle Scholar
  15. 15.
    Brüderle D, Petrovici MA, Vogginger B, Ehrlich M, Pfeil T, Millner S, Grübl A, Wendt K, Müller E, Schwartz MO, de Oliveira DH, Jeltsch S, Fieres J, Schilling M, Müller P, Breitwieser O, Petkov V, Muller L, Davison AP, Krishnamurthy P, Kremkow J, Lundqvist M, Muller E, Partzsch J, Scholze S, Zühl L, Mayr C, Destexhe A, Diesmann M, Potjans TC, Lansner A, Schüffny R, Schemmel J, Meier K (2011) A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biol Cybern 104(4–5):263–96PubMedCrossRefGoogle Scholar
  16. 16.
    Chan V, Liu S-C, Van Schaik A (2007) AER EAR: a matched silicon cochlea pair with address event representation interface. IEEE Trans Circuits Syst 54:48–59CrossRefGoogle Scholar
  17. 17.
    Chicca E, Indiveri G, Douglas R (2004) An event based VLSI network of integrate-and-fire neurons. Proceedings of IEEE international symposium on circuits and systems, pp 357–360Google Scholar
  18. 18.
    Chicca E, Indiveri G, Douglas R (2007a) Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons. In: Scholkopf B, Platt, J, Hofmann, T (eds) Advances in neural information processing systems, 19. Neural Information Processing Systems Foundation, Cambridge, pp 257–264Google Scholar
  19. 19.
    Chicca E, Whatley AM, Dante V, Lichtsteiner P, Delbruck T, Del Giudice P, Douglas R, Indiveri G (2007b) A multi-chip pulse-based neuromorphic infrastructure and its application to a model of orientation selectivity. IEEE Trans Circuits Syst 52(6):1049–1060Google Scholar
  20. 20.
    Choi TYU, Merolla PA, Arthur JV, Boahen KA, Shi BE (2005) Neuromorphic implementation of orientation hyper columns. IEEE Trans Circuits Syst 52(6):1049–1060CrossRefGoogle Scholar
  21. 21.
    Choi H, Jung H, Lee J, Yoon J, Park J, Seong D, Lee W, Hasan M, Jung GY, Hwang H (2009) An electrically modifiable synapse array of resistive switching memory. Nanotechnology 20(34):345201 (Epub)PubMedCrossRefGoogle Scholar
  22. 22.
    Chua LO (1971) Memristor—missing circuit element. IEEE Trans Circuit Theory 18(5):507–519CrossRefGoogle Scholar
  23. 23.
    Chua LO, Kang SM (1976) Memristive devices and systems. Proc IEEE 64(2):209–223CrossRefGoogle Scholar
  24. 24.
    Costas-Santos J, Serrano-Gotarredona T, Serrano-Gotarredona R, Linares-Barranco B (2007) A spatial contrast retina with on-chip calibration for neuromorphic spike-based AER vision systems. IEEE Trans Circuits Syst I 54:1444–1458CrossRefGoogle Scholar
  25. 25.
    Culurciello E, Etienne-Cummings R, Boahen KA (2003) A biomorphic digital image sensor. IEEE J Solid State Circuits 38:281–294CrossRefGoogle Scholar
  26. 26.
    Delbruck T, Mead C (1996) Analog VLSI transduction. Technical Report CNS Memo 30, California Institute of Technology and Computation and Neural Systems Program. Pasadena, CAGoogle Scholar
  27. 27.
    DeYoung MR, Findley RL, Fields C (1992) The design, fabrication, and test of a new VLSI hybrid analog-digital neural processing element. IEEE Trans Neural Netw 3(3):363–374CrossRefGoogle Scholar
  28. 28.
    Diorio C, Hasler P, Minch BA, Mead CA (1996) A single-transistor silicon synapse. IEEE Trans Electron Devices 43(11):1980–1982CrossRefGoogle Scholar
  29. 29.
    Douglas R Mahowald M (1995) Silicon neurons. In: Arbib M (ed) The handbook of brain theory and neural networks. MIT Press, Cambridg, pp 282–289Google Scholar
  30. 30.
    Douglas R, Mahowald M, Mead C (1995) Neuromorphic Analog VLSI. Annu Rev Neurosci 18:255–281PubMedCrossRefGoogle Scholar
  31. 31.
    Elias JG (1993) Artificial dendritic trees. Neural Comput 5(4):648–664CrossRefGoogle Scholar
  32. 32.
    Etienne-Cummings R, Van der Spiegel, J (1996) Neuromorphic vision sensors. Sens Actuators A Phys 56(1–2):19–29CrossRefGoogle Scholar
  33. 33.
    Faggin F, Mead C (1995) VLSI Implementation of Neural Networks. In An Introduction to Neural and Electronic Networks. Academic Press, San Diego, pp 275–292Google Scholar
  34. 34.
    Fitzhugh R (1966) An electronic model of the nerve membrane for demonstration purposes. J Appl Physiol 21:305–308PubMedGoogle Scholar
  35. 35.
    Folowosele F (2010) Neuromorphic systems: silicon neurons and neural arrays for emulating the nervous system. Neurdon. http://www.neurdon.com/2010/08/12/neuromorphic-systems-silicon-neurons-and-neural-arrays-for-emulating-the-nervous-system/Google Scholar
  36. 36.
    Folowosele F, Hamilton TJ, Etienne-Cummings R (2011) Silicon modeling of the Mihalaş--Niebur neuron. IEEE Trans Neural Netw 22(12):1915–1927Google Scholar
  37. 37.
    Fragniére E, van Schaik A, Vittoz EA (1997) Design of an analogue VLSI model of an active cochlea. Analog Integr Circuits and Signal Processing 12:19–35CrossRefGoogle Scholar
  38. 38.
    Furth P, Andreou AG (1995) A design framework for low power analog filter banks. IEEE Trans Circuits Syst 42(11):966–971CrossRefGoogle Scholar
  39. 39.
    Giulioni M, Camilleri P, Dante V, Badoni D, Indiveri G, Braun J, Del Giudice P (2008) A VLSI network of spiking neurons with plastic fully configurable “stop-learning” synapses. Proceedings of IEEE international conference on electronics, circuits and systems, pp 678–681Google Scholar
  40. 40.
    Glover M, Hamilton A, Smith LS (2002) Analogue VLSI leaky integrated-and-fire neurons and their use in a sound analysis system. Analog Integr Circuits Signal Processing 30(2):91–100CrossRefGoogle Scholar
  41. 41.
    Goldberg DH, Cauwenberghs G, Andreou AG (2001) Probabilistic synaptic weighting in a reconfigurable network of VLSI integrate-and-fire neurons. Neural Net 14:781–793CrossRefGoogle Scholar
  42. 42.
    Gorchetchnikov A, Hasselmo ME (2005) A biophysical implementation of a bidirectional graph search algorithm to solve multiple goal navigation tasks. Connect Sci 17(1–2):145–166CrossRefGoogle Scholar
  43. 43.
    Gorchetchnikov A, Versace, M, Ames H, Chandler B, Léveillé J, Livitz G, Mingolla E, Snider G, Amerson R, Carter D, Abdalla H, Qureshi MS (2011a) Review and unification of learning framework in Cog Ex Machina platform for memristive neuromorphic hardware. Proceedings of the international Joint Conference on neural networks, pp 2601–2608Google Scholar
  44. 44.
    Gorchetchnikov A, Léveillé J, Versace M, Ames HM, Livitz G, Chandler B, Mingolla E, Carter D, Amerson R, Abdalla H, Qureshi S, Snider G (2011b) MoNETA: massive parallel application of biological models navigating through virtual Morris water maze and beyond. BMC Neurosci 12(Suppl 1):310CrossRefGoogle Scholar
  45. 45.
    Grossberg S (1973) Contour enhancement, short-term memory, and constancies in reverberating neural networks. Stud Appl Math 52:213–257Google Scholar
  46. 46.
    Hafliger P (2007) Adaptive WTA with an analog VLSI neuromorphic learning chip. IEEE Trans Neural Netw 18(2):551–572PubMedCrossRefGoogle Scholar
  47. 47.
    Hamilton TJ, Jin C, van Schaik A, Tapson J (2008) An active 2-D silicon cochlea. IEEE Trans Biomed Circuits Syst 2(1):30–43CrossRefGoogle Scholar
  48. 48.
    Hodgkin AL, Huxley AF (1952) Currents carried by sodium and potassium ions through the membrane of the giant squid axon of loligo. J Phys 116:449–472Google Scholar
  49. 49.
    Hsu D, Figueroa M, Diorio C (2002) Competitive learning with floating-gate circuits. IEEE Trans Neural Netw 13:732–744PubMedCrossRefGoogle Scholar
  50. 50.
    Indiveri G (1998) Analog VLSI model of locust DCMD neuron response for computation of object approach. In: Smith L, Hamilton A (eds) Neuromorphic systems: engineering silicon from neurobiology. World Scientific, Singapore, pp 47–60CrossRefGoogle Scholar
  51. 51.
    Indiveri G, Murer R, Kramer J (2001) Active vision using an analog VLSI model of selective attention. IEEE Trans Circuits Syst II 48(5):492–500CrossRefGoogle Scholar
  52. 52.
    Indiveri G, Chicca E, Douglas RJ (2004) A VLSI reconfigurable network of integrate-and-fire neurons with spike-based learning synapses. European symposium on artificial neural networks, pp 405–410Google Scholar
  53. 53.
    Indiveri G, Chicca E, Douglas RJ (2006) A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Trans Neural Netw 17(1):211–221PubMedCrossRefGoogle Scholar
  54. 54.
    Indiveri G, Chicca E, Douglas RJ (2009) Artificial cognitive systems: from VLSI networks of spiking neurons to neuromorphic cognition. Cognitive Comput 1:119–127CrossRefGoogle Scholar
  55. 55.
    Indiveri G, Linares-Barranco B, Hamilton TJ, 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, Saïghi S, Serrano-Gotarredona T, Wijekoon J, Wang Y, Boahen K (2011) Neuromorphic silicon neuron circuits. Front Neurosci 5:73PubMedGoogle Scholar
  56. 56.
    Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans PAMI 20:1254–1260CrossRefGoogle Scholar
  57. 57.
    Izhikevich EM, Edelman GM (2008) Large-scale model of mammalian thalamo-cortical systems. PNAS 105:3593–3598PubMedCrossRefGoogle Scholar
  58. 58.
    Jo SH, Chang T, Ebong I, Bhadviya BB, Mazumder P, Lu W (2010) Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett 10(4):1297–1301PubMedCrossRefGoogle Scholar
  59. 59.
    Johnson RH, Hanna, GR (1969) Membrane model: a single transistor analog of excitable membrane. J Theor Biol 22:401–411PubMedCrossRefGoogle Scholar
  60. 60.
    Karplus WJ, Soroka WW (1959) Analog Methods: computation and Simulation. McGraw-Hill, New YorkGoogle Scholar
  61. 61.
    Kogge P (2011) The tops in FLOPS. IEEE Spectr 48(2):48–54CrossRefGoogle Scholar
  62. 62.
    Koickal TJ, Hamilton A, Tan SL, Covington JA, Gardner JW, Pearce TC (2005) Analog VLSI circuit implementation of an adaptive neuromorphic olfaction chip. IEEE Int Symp Circuits Syst 54:60–73Google Scholar
  63. 63.
    Lapique L (1907) Sur l’excitation electrique des nerfs. J Physiol 9:620–635Google Scholar
  64. 64.
    Lazzaro J, Mead C (1989a) Silicon modeling of pitch perception. Proc Natl Acad Sci USA 86(23):9597–9601CrossRefGoogle Scholar
  65. 65.
    Lazzaro J, Mead C (1989b) A silicon model of auditory localization. Neural Comput 1(1):47–57CrossRefGoogle Scholar
  66. 66.
    Lazzaro J, Wawrzynek J (1997) Speech recognition experiments with silicon auditory models. Analog Integr Circuits 13:37–51CrossRefGoogle Scholar
  67. 67.
    Léveillé J, Ames H, Chandler B, Gorchetchnikov A, Livitz G, Versace M Mingolla E (2011) Object recognition and localization in a virtual animat: large-scale implementation in dense memristive memory devices. Proceedings of the international joint conference on neural networksGoogle Scholar
  68. 68.
    Lewis ER (1968) An electronic model of the neuroelectric point process. Kybernetik 5:30–46PubMedCrossRefGoogle Scholar
  69. 69.
    Lichtsteiner P, Posch C, Delbruck T (2008) A 128 × 128 × 120db 15 μs latency asynchronous temporal contrast vision detector. IEEE J Solid-State Circuits 43(2):566–576CrossRefGoogle Scholar
  70. 70.
    Liu W, Andreou AG, Goldstein MH, Jr (1993a) Analog cochlear model for multire solution speech analysis. Adv Neural Inf Processing Syst 5:666–673Google Scholar
  71. 71.
    Liu W, Andreou AG, Goldstein MH, Jr (1993b) Voiced speech representation by an analog silicon model of the auditory periphery. IEEE Trans on Neural Net 3(3):477–487CrossRefGoogle Scholar
  72. 72.
    Liu S-C, Delbruck T (2010) Neuromorphic sensory systems. Curr Opin Neurobio 20:288–295CrossRefGoogle Scholar
  73. 73.
    Liu S-C, Kramer J, Indiveri G, Delbruck T, Douglas R (2002) Analog VLSI: circuits and principles. MIT Press, CambridgeGoogle Scholar
  74. 74.
    Liu S-C, Mesgarani N, Harris J, Hermansky H (2010) The use of spike-based representations for hardware auditory systems. IEEE International symposium on circuits and systems, pp 505–508Google Scholar
  75. 75.
    Livitz G, Ames H, Chandler B, Gorchetchnikov A, Léveillé J, Vasilkoski Z, Versace M, Mingolla E, Snider G, Amerson R, Carter D, Abdalla H, Qureshi MS (2011) Visually-guided adaptive robot (ViGuAR). Proceedings of the international joint conference on neural networks, pp 2944–2951Google Scholar
  76. 76.
    Lyon RF, Mead C (1988) An analog electronic cochlea. IEEE Trans Acoust 36(7):1119–1134CrossRefGoogle Scholar
  77. 77.
    Mahowald M, Douglas R (1991) A silicon neuron. Nature 354(6354):515–518PubMedCrossRefGoogle Scholar
  78. 78.
    Markram H (2006) The blue brain project. Nat Rev Neurosci 7:153–160PubMedCrossRefGoogle Scholar
  79. 79.
    McKenzie A, Branch DW, Forsythe C, James CD (2010) Toward exascale computing through neuromorphic approaches. Sandia Report SAND2010-6312, Sandia National LaboratoriesGoogle Scholar
  80. 80.
    Mead C (1989) Analog VLSI and neural systems. Addison-Wesley, BostonGoogle Scholar
  81. 81.
    Mead C, Mahowald MA (1988) A silicon model of early visual processing. Neural Netw 1(1):91–97CrossRefGoogle Scholar
  82. 82.
    Merolla PA, Arthur JV, Shi BE, Boahen KA (2007) Expandable networks for neuromorphic chips. IEEE Trans Circuits Syst I: Fundam Theory Appl 54(2):301–311CrossRefGoogle Scholar
  83. 83.
    Minch BA, Hasler P, Diorio C, Mead C (1995) A silicon axon. In: Tesauro G, Touretzky DS, Leen TK (eds) Adv Neural Inf Processing Syst 7. MIT Press, Cambridge, pp 739–746Google Scholar
  84. 84.
    Mitra S, Fusi S, Indiveri G (2009) Real-time classification of complex patterns using spike-based learning in neuromorphic VLSI. IEEE Trans Biomed Circuits Syst 3(1):32–42CrossRefGoogle Scholar
  85. 85.
    Morris RGM (1981) Spatial localization does not require the presence of local cues. Learn Motiv 12(2):239–260CrossRefGoogle Scholar
  86. 86.
    Navaridas J, Lujan M, Miguel-Alonso J, Plana LA, Furber S (2009) Understanding the interconnection network of SpiNNaker. Proceedings of the international conference on supercomputing, p 286Google Scholar
  87. 87.
    Nogaret A, Lambert NJ, Bending SJ Austin J (2004) Artificial ion channels and spike computation in modulation-doped semiconductors. Europhys Lett 68(6):874–880CrossRefGoogle Scholar
  88. 88.
    Northmore DPM. Elias JG (1996) Spike train processing by a silicon neuromorph: the role of sub linear summation in dendrites. Neural Comput 8(6):1245–1265PubMedCrossRefGoogle Scholar
  89. 89.
    Oster M, Liu SC (2004) A winner-take-all spiking network with spiking inputs. Proceedings of 11th IEEE international conference on electronics, circuits, and systems, pp 1051–1058Google Scholar
  90. 90.
    Pearce TC (1997) Computational parallels between the biological olfactory pathway and its analogue ‘the electric nose’: sensor based machine olfaction. Biosystems 41(2):69–90PubMedCrossRefGoogle Scholar
  91. 91.
    Pearson M, Nibouche M, Gilhespy I, Gurney K, Melhuish C, Mitchison B, Pipe AG (2006) A hardware based implementation of a tactile sensory system for neuromorphic signal processing applications. Proceedings of IEEE international conference on acoustics, speech, and signal processing, p 4Google Scholar
  92. 92.
    Pickett MD, Strukov DB, Borghetti JL, Yang JJ, Snider GS, Stewart DR, Williams RS (2009) Switching dynamics in titanium dioxide memristive devices. J Appl Phys 106(7):074508CrossRefGoogle Scholar
  93. 93.
    Posch C, Matolin D, Wohlgenannt R (2010) A QVGA 143 dB DR asynchronous address-event PWM dynamic image sensor with lossless pixel-level video compression. ISSCC digest of technical papers, pp 400–401Google Scholar
  94. 94.
    Rasche C, Douglas RJ (2000) An improved silicon neuron. Analog Integr 23(3):227–236CrossRefGoogle Scholar
  95. 95.
    Rasche C, Douglas RJ (2001) Forward- and back propagation in a silicon dendrite. IEEE Trans Neural Netw 12(2):386–393PubMedCrossRefGoogle Scholar
  96. 96.
    Rasche C, Douglas RJ, Mahowald M (1998) Characterization of a silicon pyramidal neuron. In: Smith LS, Hamilton A (eds) Neuromorphic systems: engineering silicon from neurobiology. World Scientific, Singapore, pp 169–177CrossRefGoogle Scholar
  97. 97.
    Roy G (1972) A simple electronic analog of the squid axon membrane: the neuro FET. IEEE Trans Biomed Eng BME-18:60–63CrossRefGoogle Scholar
  98. 98.
    Roy, D (2006) Design and developmental metrics of a ‘skin-like’ mutli-input quasi-compliant robotic gripper sensor using tactile matrix. J Intell Robot Syst 46(4):305–337CrossRefGoogle Scholar
  99. 99.
    Ruedi PF, Heim P, Kaess F, Grenet E, Heitger F, Burgi PY, Gyger S, Nussbaum P (2003) A 128 × 128 pixel 120-dB dynamic-range vision-sensor chip for image contrast and orientation extraction. IEEE J Solid-State Circuits 38:2325–2333CrossRefGoogle Scholar
  100. 100.
    Runge RG, Uemura M, Viglione SS (1968) Electronic synthesis of the avian retina. IEEE Trans Biomed Eng BME-15:138–151PubMedCrossRefGoogle Scholar
  101. 101.
    Russell A, Orchard G, Dong Y, Mihalas S, Niebur E, Tapson J, Etienne-Cummings R (2010) optimization methods for spiking neurons and networks. IEEE Trans Neural Netw 21(12):1950–1962PubMedCrossRefGoogle Scholar
  102. 102.
    Samardak A, Nogaret A, Taylor S, Austin J, Farrer I, Ritchie DA (2008) An analogue sum and threshold neuron based on the quantum tunneling amplification of neural pulses. New J Phys 10Google Scholar
  103. 103.
    Schemmel J, Fieres J, Meier K (2008) Wafer-scale integration of analog neural networks. Proceedings of the IEEE joint conference on neural networks, pp 431–438Google Scholar
  104. 104.
    Serrano-Gotarredona R, Oster M, Lichtsteiner P, Linares-Barranco A, Paz-Vicente R, Gomez-Rodriguez F, Riss HK, Delbruck T, Liu S-C, Zahnd S, Whatley AM, Douglas R, Hafliger P, Jimenz-Moreno G, Civit A, Serrano-Gotarredona T, Acosta-Jimenez A, Linares-Barranco B (2006) AER building blocks for multi-layer multi-chip neuromorphic vision systems. In: Becker S, Thrun S, Obermayer K (eds) Advances in neural information processing systems 15. MIT Press, Cambridge, pp 1217–1224Google Scholar
  105. 105.
    Serrano-Gotarredona R, Oster M, Lichtsteiner P, Linares-Barranco A, Paz-Vicente R, Gomez-Rodriguez F, Camunas-Mesa L, Berner R, Rivas M, Delbruck T, Liu S-C, Douglas R, Hafliger P, Jimenez-Moreno G, Civit A, Serrano-Gotarredona T, Acosta-Jimenez A, Lineares-Barranco B (2009) CAVIAR: a 45 k-neuron, 5 M-synapse, 12G-connects/s AER hardware sensory-processing-learning-actuating system for high speed visual object recognition and tracking. IEEE Trans Neural Netw 20(9):1417–1438PubMedCrossRefGoogle Scholar
  106. 106.
    Shurmer HV, Gardner JW (1992).Odor discrimination with an electric nose. Sens Actuators B-Chemical, 8(11):1–11Google Scholar
  107. 107.
    Smith LS (2008) Neuromorphic systems: past, present, and future. In: Hussain A et al., (eds) Brain inspired cognitive systems, advances in experimental medicine and biology, 657. MIT Press, Cambridge, pp 167–182Google Scholar
  108. 108.
    Snider GS (2007) Self-organized computation with unreliable, memristive nanodevices. Nanotechnology 18(36):36502CrossRefGoogle Scholar
  109. 109.
    Snider GS (2008) Spike-timing-dependent learning in memristive nanodevices. IEEE/ACM International symposium on nanoscale architectures, pp 85–92Google Scholar
  110. 110.
    Snider GS (2011) Instar and outstar learning with memristive nanodevices. Nanotechnology 22:015201PubMedCrossRefGoogle Scholar
  111. 111.
    Snider G, Amerson R, Carter D, Abdalla H, Qureshi S, Léveillé J, Versace M, Ames H, Patrick S, Chandler B, Gorchetchnikov A, Mingolla E (2011) Adaptive computation with memristive memory. IEEE Comput 44(2):21–28CrossRefGoogle Scholar
  112. 112.
    Strukov DB, Snider GS, Stewart DR, Williams SR (2008) The missing memristor found. Nature 453:80–83PubMedCrossRefGoogle Scholar
  113. 113.
    Vainbrand D, Ginosar R (2010) Network-on-chip architectures for neural networks. IEEE international symposium on networks-on-chip, pp 135–144Google Scholar
  114. 114.
    Van Schaik A (2001) Building blocks for electronic spiking neural networks. Neural Netw 14(6–7):617–628PubMedCrossRefGoogle Scholar
  115. 115.
    Van Schaik A, Vittoz E (1997) A silicon model of amplitude modulation detection in the auditory brainstem. Adv NIPS 9:741–747Google Scholar
  116. 116.
    Vasarhelyi G, Adam M, Vazsonyi E, Kis A, Barsony I, Ducso C (2006) Characterization of an integrable single-crystalline 3-D tactile sensor. IEEE Sens J 6(4):928–934CrossRefGoogle Scholar
  117. 117.
    Versace M Chandler B (2010) MoNETA: a mind made from memristors. IEEE Spectr 12:30–37CrossRefGoogle Scholar
  118. 118.
    Vogelstein R, Malik U, Culurciello E, Cauwenberghs G, Etienne-Cummings R (2007a) A multichip neuromorphic system for spike-based visual information processing. Neural Comput 19(9):2281–2300CrossRefGoogle Scholar
  119. 119.
    Vogelstein R, Malik U, Vogelstein J, Cauwenberghs G (2007b) Dynamically reconfigurable silicon array of spiking neurons with conductance-based synapses. IEEE Trans Neural Netw 18(1):253–265CrossRefGoogle Scholar
  120. 120.
    Watts L, Kerns D, Lyon R, Mead C (1992) Improved implementation of the silicon cochlea. IEEE J Solid-State Circ 27(5):692–700CrossRefGoogle Scholar
  121. 121.
    Wijekoon, JHB, Dudek, P (2008) Compact silicon neuron circuit with spiking and bursting behavior. Neural Netw 21:524–534PubMedCrossRefGoogle Scholar
  122. 122.
    Wolpert S, Micheli-Tzanakou E (1996) A neuromime in VLSI. IEEE Trans Neural Netw 7(2):300–306PubMedCrossRefGoogle Scholar
  123. 123.
    Xia Q, Robinett W, Cumbie MW, Banerjee N, Cardinali TJ, Yang JJ, Wu W, Li X, Tong WM, Strukov DB, Snider GS, Medeiros-Ribeiro G, Williams RS (2009) Memristor/CMOS hybrid integrated circuits for reconfigurable logic. Nano Lett 9(10):3640–3645PubMedCrossRefGoogle Scholar
  124. 124.
    Yang Z, Murray AF, Woergoetter F, Cameron KL, Boonobhak V (2006) A neuromorphic depth-from-motion vision model with STDP adaptation. IEEE Trans Neural Netw 17(2):482–495PubMedCrossRefGoogle Scholar
  125. 125.
    Yang JJ, Pickett MD, Li X, Ohlberg DAA, Stewart DR, Williams RS (2008) Memristive switching mechanism for metal/oxide/metal nanodevices. Nature Nanotechnol 3:429–433CrossRefGoogle Scholar
  126. 126.
    Zaghloul KA, Boahen K (2006) A silicon retina that reproduces signals in the optic nerve. J Neural Eng 3:257–267PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Heather Ames
    • 1
  • Massimiliano Versace
    • 1
  • Anatoli Gorchetchnikov
    • 1
  • Benjamin Chandler
    • 2
  • Gennady Livitz
    • 1
  • Jasmin Léveillé
    • 1
  • Ennio Mingolla
    • 1
  • Dick Carter
    • 2
  • Hisham Abdalla
    • 2
  • Greg Snider
    • 2
  1. 1.Neuromorphics Lab, Center of Excellence for Learning in Education, Science, and Technology (CELEST)Boston UniversityBostonUSA
  2. 2.HP LabsPalo AltoUSA

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