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.
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Notes
- 1.
http://facets.kip.uni-heidelberg.de/public/motivation/index.html
- 2.
http://brainscales.kip.uni-heidelberg.de/index.html
- 3.
http://facets.kip.uni-heidelberg.de/images/4/48/Public–FACETS_15879_Summary-flyer.pdf
- 4.
http://etienne.ece.jhu.edu/projects/ifat/index.html
- 5.
http://nl.bu.edu/
References
Abrahamsen J, Hafliger P, Lande T (2004) A time domain winner-take-all network of integrate-and-fire neurons. IEEE Int Symp Circuits Syst 5:361–364
Afifi A, Ayatollahi A, Raissi F (2009) STDP implementation using memristive nano device in CMOS-Nano neuromorphic networks. IEICE Electron Express 6(3):148–153
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)
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 1–12
Andreou AG, Meitzler RC, Strohben K, Boahen KA (1995) Analog VLSI neuromorphic image acquisition and pre-processing systems. Neural Net 8(7–8):1323–1347
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 84:1749–1753
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 1–8
Bartolozzi C, Indiveri G (2007) Synpatic dynamics in analog VLSI. Neural Comput 19(10):2581–2603
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 e1000748
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)
Bernabe K (1999) A throughput-on-demand address-event transmitter for neuromorphic chips. Advanced Research in VLSI, pp 72–86
Boahen K (2007) Synchrony in silicon: the gamma rhythm. IEEE Trans Neural Netw 18(6):1815–1825
Boahen K, Andreou A (1992) A contrast sensitive silicon retina with reciprocal synapses. Adv Neural Inf Process Syst 4:764–772
Brockman WH (1979) A simple electronic neuron model incorporating both active and passive responses. IEEE Trans Biomed Eng BME-26:635–639
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):263–96
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 54:48–59
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 357–360
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 257–264
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):1049–1060
Choi TYU, Merolla PA, Arthur JV, Boahen KA, Shi BE (2005) Neuromorphic implementation of orientation hyper columns. IEEE Trans Circuits Syst 52(6):1049–1060
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)
Chua LO (1971) Memristor—missing circuit element. IEEE Trans Circuit Theory 18(5):507–519
Chua LO, Kang SM (1976) Memristive devices and systems. Proc IEEE 64(2):209–223
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 54:1444–1458
Culurciello E, Etienne-Cummings R, Boahen KA (2003) A biomorphic digital image sensor. IEEE J Solid State Circuits 38:281–294
Delbruck T, Mead C (1996) Analog VLSI transduction. Technical Report CNS Memo 30, California Institute of Technology and Computation and Neural Systems Program. Pasadena, CA
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):363–374
Diorio C, Hasler P, Minch BA, Mead CA (1996) A single-transistor silicon synapse. IEEE Trans Electron Devices 43(11):1980–1982
Douglas R Mahowald M (1995) Silicon neurons. In: Arbib M (ed) The handbook of brain theory and neural networks. MIT Press, Cambridg, pp 282–289
Douglas R, Mahowald M, Mead C (1995) Neuromorphic Analog VLSI. Annu Rev Neurosci 18:255–281
Elias JG (1993) Artificial dendritic trees. Neural Comput 5(4):648–664
Etienne-Cummings R, Van der Spiegel, J (1996) Neuromorphic vision sensors. Sens Actuators A Phys 56(1–2):19–29
Faggin F, Mead C (1995) VLSI Implementation of Neural Networks. In An Introduction to Neural and Electronic Networks. Academic Press, San Diego, pp 275–292
Fitzhugh R (1966) An electronic model of the nerve membrane for demonstration purposes. J Appl Physiol 21:305–308
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/
Folowosele F, Hamilton TJ, Etienne-Cummings R (2011) Silicon modeling of the Mihalaş--Niebur neuron. IEEE Trans Neural Netw 22(12):1915–1927
Fragniére E, van Schaik A, Vittoz EA (1997) Design of an analogue VLSI model of an active cochlea. Analog Integr Circuits and Signal Processing 12:19–35
Furth P, Andreou AG (1995) A design framework for low power analog filter banks. IEEE Trans Circuits Syst 42(11):966–971
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 678–681
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):91–100
Goldberg DH, Cauwenberghs G, Andreou AG (2001) Probabilistic synaptic weighting in a reconfigurable network of VLSI integrate-and-fire neurons. Neural Net 14:781–793
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):145–166
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 2601–2608
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):310
Grossberg S (1973) Contour enhancement, short-term memory, and constancies in reverberating neural networks. Stud Appl Math 52:213–257
Hafliger P (2007) Adaptive WTA with an analog VLSI neuromorphic learning chip. IEEE Trans Neural Netw 18(2):551–572
Hamilton TJ, Jin C, van Schaik A, Tapson J (2008) An active 2-D silicon cochlea. IEEE Trans Biomed Circuits Syst 2(1):30–43
Hodgkin AL, Huxley AF (1952) Currents carried by sodium and potassium ions through the membrane of the giant squid axon of loligo. J Phys 116:449–472
Hsu D, Figueroa M, Diorio C (2002) Competitive learning with floating-gate circuits. IEEE Trans Neural Netw 13:732–744
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 47–60
Indiveri G, Murer R, Kramer J (2001) Active vision using an analog VLSI model of selective attention. IEEE Trans Circuits Syst II 48(5):492–500
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 405–410
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):211–221
Indiveri G, Chicca E, Douglas RJ (2009) Artificial cognitive systems: from VLSI networks of spiking neurons to neuromorphic cognition. Cognitive Comput 1:119–127
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 5:73
Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans PAMI 20:1254–1260
Izhikevich EM, Edelman GM (2008) Large-scale model of mammalian thalamo-cortical systems. PNAS 105:3593–3598
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):1297–1301
Johnson RH, Hanna, GR (1969) Membrane model: a single transistor analog of excitable membrane. J Theor Biol 22:401–411
Karplus WJ, Soroka WW (1959) Analog Methods: computation and Simulation. McGraw-Hill, New York
Kogge P (2011) The tops in FLOPS. IEEE Spectr 48(2):48–54
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 54:60–73
Lapique L (1907) Sur l’excitation electrique des nerfs. J Physiol 9:620–635
Lazzaro J, Mead C (1989a) Silicon modeling of pitch perception. Proc Natl Acad Sci USA 86(23):9597–9601
Lazzaro J, Mead C (1989b) A silicon model of auditory localization. Neural Comput 1(1):47–57
Lazzaro J, Wawrzynek J (1997) Speech recognition experiments with silicon auditory models. Analog Integr Circuits 13:37–51
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 networks
Lewis ER (1968) An electronic model of the neuroelectric point process. Kybernetik 5:30–46
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):566–576
Liu W, Andreou AG, Goldstein MH, Jr (1993a) Analog cochlear model for multire solution speech analysis. Adv Neural Inf Processing Syst 5:666–673
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):477–487
Liu S-C, Delbruck T (2010) Neuromorphic sensory systems. Curr Opin Neurobio 20:288–295
Liu S-C, Kramer J, Indiveri G, Delbruck T, Douglas R (2002) Analog VLSI: circuits and principles. MIT Press, Cambridge
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 505–508
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 2944–2951
Lyon RF, Mead C (1988) An analog electronic cochlea. IEEE Trans Acoust 36(7):1119–1134
Mahowald M, Douglas R (1991) A silicon neuron. Nature 354(6354):515–518
Markram H (2006) The blue brain project. Nat Rev Neurosci 7:153–160
McKenzie A, Branch DW, Forsythe C, James CD (2010) Toward exascale computing through neuromorphic approaches. Sandia Report SAND2010-6312, Sandia National Laboratories
Mead C (1989) Analog VLSI and neural systems. Addison-Wesley, Boston
Mead C, Mahowald MA (1988) A silicon model of early visual processing. Neural Netw 1(1):91–97
Merolla PA, Arthur JV, Shi BE, Boahen KA (2007) Expandable networks for neuromorphic chips. IEEE Trans Circuits Syst I: Fundam Theory Appl 54(2):301–311
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 739–746
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):32–42
Morris RGM (1981) Spatial localization does not require the presence of local cues. Learn Motiv 12(2):239–260
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 286
Nogaret A, Lambert NJ, Bending SJ Austin J (2004) Artificial ion channels and spike computation in modulation-doped semiconductors. Europhys Lett 68(6):874–880
Northmore DPM. Elias JG (1996) Spike train processing by a silicon neuromorph: the role of sub linear summation in dendrites. Neural Comput 8(6):1245–1265
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 1051–1058
Pearce TC (1997) Computational parallels between the biological olfactory pathway and its analogue ‘the electric nose’: sensor based machine olfaction. Biosystems 41(2):69–90
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 4
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):074508
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 400–401
Rasche C, Douglas RJ (2000) An improved silicon neuron. Analog Integr 23(3):227–236
Rasche C, Douglas RJ (2001) Forward- and back propagation in a silicon dendrite. IEEE Trans Neural Netw 12(2):386–393
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 169–177
Roy G (1972) A simple electronic analog of the squid axon membrane: the neuro FET. IEEE Trans Biomed Eng BME-18:60–63
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):305–337
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 38:2325–2333
Runge RG, Uemura M, Viglione SS (1968) Electronic synthesis of the avian retina. IEEE Trans Biomed Eng BME-15:138–151
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):1950–1962
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 10
Schemmel J, Fieres J, Meier K (2008) Wafer-scale integration of analog neural networks. Proceedings of the IEEE joint conference on neural networks, pp 431–438
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 1217–1224
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):1417–1438
Shurmer HV, Gardner JW (1992).Odor discrimination with an electric nose. Sens Actuators B-Chemical, 8(11):1–11
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 167–182
Snider GS (2007) Self-organized computation with unreliable, memristive nanodevices. Nanotechnology 18(36):36502
Snider GS (2008) Spike-timing-dependent learning in memristive nanodevices. IEEE/ACM International symposium on nanoscale architectures, pp 85–92
Snider GS (2011) Instar and outstar learning with memristive nanodevices. Nanotechnology 22:015201
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):21–28
Strukov DB, Snider GS, Stewart DR, Williams SR (2008) The missing memristor found. Nature 453:80–83
Vainbrand D, Ginosar R (2010) Network-on-chip architectures for neural networks. IEEE international symposium on networks-on-chip, pp 135–144
Van Schaik A (2001) Building blocks for electronic spiking neural networks. Neural Netw 14(6–7):617–628
Van Schaik A, Vittoz E (1997) A silicon model of amplitude modulation detection in the auditory brainstem. Adv NIPS 9:741–747
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):928–934
Versace M Chandler B (2010) MoNETA: a mind made from memristors. IEEE Spectr 12:30–37
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):2281–2300
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):253–265
Watts L, Kerns D, Lyon R, Mead C (1992) Improved implementation of the silicon cochlea. IEEE J Solid-State Circ 27(5):692–700
Wijekoon, JHB, Dudek, P (2008) Compact silicon neuron circuit with spiking and bursting behavior. Neural Netw 21:524–534
Wolpert S, Micheli-Tzanakou E (1996) A neuromime in VLSI. IEEE Trans Neural Netw 7(2):300–306
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):3640–3645
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):482–495
Yang JJ, Pickett MD, Li X, Ohlberg DAA, Stewart DR, Williams RS (2008) Memristive switching mechanism for metal/oxide/metal nanodevices. Nature Nanotechnol 3:429–433
Zaghloul KA, Boahen K (2006) A silicon retina that reproduces signals in the optic nerve. J Neural Eng 3:257–267
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.
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Ames, H. et al. (2012). Persuading Computers to Act More Like Brains. In: Kozma, R., Pino, R., Pazienza, G. (eds) Advances in Neuromorphic Memristor Science and Applications. Springer Series in Cognitive and Neural Systems, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4491-2_4
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