Abstract
The human brain is the most powerful computational machine in this world that has inspired artificial intelligence for many years. One of the latest outcomes of the reverse engineering neural system is deep learning, which emulates the multiple-layer structure of biological neural networks. Deep learning has achieved a variety of unprecedented successes in a large range of cognitive tasks. However, accompanied by the achievements, the shortcomings of deep learning are becoming more and more severe. These drawbacks include the demand for massive data, energy inefficiency, incomprehensibility, etc. One of the innate drawbacks of deep learning is that it implements artificial intelligence through the algorithms and software alone with no consideration of the potential limitations of computational resources. On the contrary, neuromorphic computing, also known as brain-inspired computing, emulates the biological neural networks through a software and hardware co-design approach and aims to break the shackles from the von Neumann architecture and digital representation of information within it. Thus, neuromorphic computing offers an alternative approach for next-generation AI that balances computational complexity, energy efficiency, biological plausibility, and intellectual competence. This chapter aims to comprehensively introduce neuromorphic computing from the fundamentals of biological neural systems, neuron models, to hardware implementations. Lastly, critical challenges and opportunities in neuromorphic computing are discussed.
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References
Kandel, E.R., Schwartz, J.H., Jessell, T.M., Siegelbaum, S.A., Hudspeth, A.: Principles of Neural Science. McGraw-Hill, New York (2000)
Baird, E., Srinivasan, M.V., Zhang, S., Cowling, A.: Visual control of flight speed in honeybees. J. Exp. Biol. 208(20), 3895–3905 (2005)
Kern, R., Boeddeker, N., Dittmar, L., Egelhaaf, M.: Blowfly flight characteristics are shaped by environmental features and controlled by optic flow information. J. Exp. Biol. 215(14), 2501–2514 (2012)
Haenlein, M., Kaplan, A.: A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif. Manag. Rev. 61(4), 5–14 (2019)
A. M. Turing, Computing machinery and intelligence," in Parsing the Turing Test: Springer, Dordrecht 2009, pp. 23–65
Weizenbaum, J.: ELIZA—a computer program for the study of natural language communication between man and machine. Commun. ACM. 9(1), 36–45 (1966)
Campbell, M., Hoane Jr., A.J., Hsu, F.-H.: Deep blue. Artif. Intell. 134(1–2), 57–83 (2002)
Goodfellow, I., Yoshua, B., Aaron, C.: Deep Learning, p. 785 (2016). https://doi.org/10.1016/B978-0-12-391420-0.09987-X
Bengio, Y., Goodfellow, I., Courville, A.: Deep Learning. MIT Press, Cambridge, MA (2017)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)
Aron, J.: How innovative is Apple’s new voice assistant, Siri? ed: Elsevier (2011)
Hoy, M.B.: Alexa, Siri, Cortana, and more: an introduction to voice assistants. Med. Ref. Serv. Q. 37(1), 81–88 (2018)
Greenblatt, N.A.: Self-driving cars and the law. IEEE Spectr. 53(2), 46–51 (2016)
Pedrycz, W., Chen, S.-M.: Deep Learning : Algorithms and Applications. Springer, Cham (2020)
Gibney, E.: Google AI algorithm masters ancient game of Go. Nat. News. 529(7587), 445 (2016)
Mead, C.: Neuromorphic electronic systems. Proc. IEEE. 78(10), 1629–1636 (1990)
Soediono, B.: The handbook of brain theory and neural networks. J. Chem. Inf. Model. 53, 719–725 (1989). https://doi.org/10.1017/CBO9781107415324.004
y Cajal, S.R.: Comparative Study of the Sensory Areas of the Human Cortex, Clark University, Worcester (1899)
Bear, M.F., Connors, B.W., Paradiso, M.A.: Neuroscience. Lippincott Williams & Wilkins, Philadelphia (2007)
P. I. Pavlov, "Conditioned reflexes: an investigation of the physiological activity of the cerebral cortex," Ann. Neurosci., vol. 17, no. 3, p. 136, Jul 2010, doi: https://doi.org/10.5214/ans.0972-7531.1017309
H. An, An, Q., Yi, Y.: Realizing behavior level associative memory learning through three-dimensional Memristor-based neuromorphic circuits. In: IEEE Transactions on Emerging Topics in Computational Intelligence (2019)
Brunel, N., Van Rossum, M.C.: Lapicque’s 1907 paper: from frogs to integrate-and-fire. Biol. Cybern. 97(5), 337–339 (2007)
Orhan, E.: The Leaky Integrate-and-Fire Neuron Model, pp. 1–6 (2012)
Fuortes, M., Mantegazzini, F.: Interpretation of the repetitive firing of nerve cells. J. Gen. Physiol. 45(6), 1163–1179 (1962)
Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. Bull. Math. Biol. 52, 25–71 (1990). https://doi.org/10.1007/BF02459568
Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14, 1569–1572 (2003). https://doi.org/10.1109/TNN.2003.820440
Darian-Smith, I., Johnson, K., Dykes, R.: “Cold” fiber population innervating palmar and digital skin of the monkey: responses to cooling pulses. J. Neurophysiol. 36(2), 325–346 (1973)
Adrian, E.D.: The impulses produced by sensory nerve endings: part I. J. Physiol. 61(1), 49–72 (1926)
Panzeri, S., Brunel, N., Logothetis, N.K., Kayser, C.: Sensory neural codes using multiplexed temporal scales. Trends Neurosci. 33, 111–120 (2010). https://doi.org/10.1016/j.tins.2009.12.001
Zhao, C., Yi, Y., Li, J., Fu, X., Liu, L.: Interspike-interval-based analog spike-time-dependent encoder for neuromorphic processors. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 25, 2193–2205 (2017). https://doi.org/10.1109/TVLSI.2017.2683260
Zhao, C., et al.: Energy efficient temporal spatial information processing circuits based on STDP and spike iteration. IEEE Trans. Circuits Syst. II. 67(10), 1715–1719 (2019)
Averbeck, B.B., Latham, P.E., Pouget, A.: Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7(5), 358–366 (2006)
Pasupathy, A., Connor, C.E.: Population coding of shape in area V4. Nat. Neurosci. 5(12), 1332–1338 (2002)
Panzeri, S., Macke, J.H., Gross, J., Kayser, C.: Neural population coding: combining insights from microscopic and mass signals. Trends Cogn. Sci. 19(3), 162–172 (2015)
Pouget, A., Dayan, P., Zemel, R.: Information processing with population codes. Nat. Rev. Neurosci. 1(2), 125–132 (2000)
Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)
Liu, J.-H., Wang, C.-Y., An, Y.-Y.: A Survey of neuromorphic vision system: biological nervous systems realized on silicon. In: 2009 International Conference on Industrial Mechatronics and Automation, IEEE, pp. 154–157 (2009)
Indiveri, G., et al.: Neuromorphic silicon neuron circuits (in English). Front. Neurosci., Review 5 (2011, May 31) https://doi.org/10.3389/fnins.2011.00073
Poon, C.S., Zhou, K.: Neuromorphic silicon neurons and large-scale neural networks: challenges and opportunities. Front. Neurosci. 5, 2009–2011 (2011). https://doi.org/10.3389/fnins.2011.00108
Ahmed, M.R., Sujatha, B.K.: A review on methods, issues and challenges in neuromorphic engineering. In: 2015 International Conference on Communications and Signal Processing (ICCSP), pp. 899–903 (2015). https://doi.org/10.1109/ICCSP.2015.7322626
Schuman, C.D., Ridge, O., Disney, A.: Dynamic adaptive neural network arrays: a neuromorphic architecture. In: Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments – MLHPC’15, pp. 1–4 (2015). https://doi.org/10.1145/2834892.2834895
Yi, Y., et al.: FPGA based spike-time dependent encoder and reservoir design in neuromorphic computing processors. Microprocess. Microsyst. 46, 175–183 (2016). https://doi.org/10.1016/j.micpro.2016.03.009
Sun, J.: CMOS and Memristor Technologies for Neuromorphic Computing Applications (2015)
Babacan, Y., Kaçar, F., Gürkan, K.: A spiking and bursting neuron circuit based on memristor. Neurocomputing. 203, 86–91 (2016). https://doi.org/10.1016/j.neucom.2016.03.060
An, H., Ehsan, M.A., Zhou, Z., Shen, F., Yi, Y.: Monolithic 3D neuromorphic computing system with hybrid CMOS and memristor-based synapses and neurons. Integr. VLSI J. (2017)
An, H., Al-Mamun, M.S., Orlowski, M., Yi, Y.: A three-dimensional (3D) Memristive Spiking Neural Network (M-SNN) system. In: International Symposium on Quality Electronic Design (2021)
An, H., Ha, D.S., Yi, Y.: Powering next-generation industry 4.0 by a self-learning and low-power neuromorphic system. In: Proceedings of the 7th ACM International Conference on Nanoscale Computing and Communication, pp. 1–6 (2020)
An, H.: Powering Next-Generation Artificial Intelligence by Designing Three-Dimensional High-Performance Neuromorphic Computing System with Memristors. Virginia Tech (2020)
Mead, C.: How we created neuromorphic engineering. Nat. Electron. 3(7), 434–435 (2020)
Izhikevich, E.M.: Dynamical systems in neuroscience computational neuroscience. Dyn. Syst. 25, 227–256 (2007). https://doi.org/10.1017/S0143385704000173
Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15(5), 1063–1070 (2004)
Brette, R., Gerstner, W.: Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J. Neurophysiol. 94(5), 3637–3642 (2005)
Jolivet, R., Lewis, T.J., Gerstner, W.: Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy. J. Neurophysiol. 92(2), 959–976 (2004)
Livi, P., Indiveri, G.: A current-mode conductance-based silicon neuron for address-event neuromorphic systems. In: 2009 IEEE international symposium on circuits and systems, IEEE, pp. 2898–2901 (2009)
Wijekoon, J.H.B., Dudek, P.: Compact silicon neuron circuit with spiking and bursting behaviour. Neural Netw. 21, 524–534 (2008). https://doi.org/10.1016/j.neunet.2007.12.037
Van Schaik, A., Jin, C., McEwan, A., Hamilton, T.J., Mihalas, S., Niebur, E.: A log-domain implementation of the Mihalas-Niebur neuron model. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, IEEE, pp. 4249–4252 (2010)
Schaik, V., Jin, C., McEwan, A., Hamilton, T.J.: A log-domain implementation of the Izhikevich neuron model. In: ISCAS 2010–2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, pp. 4253–4256 (2010). doi: https://doi.org/10.1109/ISCAS.2010.5537564
Ma, Q., Haider, M.R., Shrestha, V.L., Massoud, Y.: Bursting Hodgkin–Huxley model-based ultra-low-power neuromimetic silicon neuron. Analog Integr. Circ. Sig. Process. 73(1), 329–337 (2012)
Yu, T., Sejnowski, T.J., Cauwenberghs, G.: Biophysical neural spiking, bursting, and excitability dynamics in reconfigurable analog VLSI. IEEE Trans. Biomed. Circuits Syst. 5(5), 420–429 (2011)
Abbott, L.F.: Lapicque’s introduction of the integrate-and-fire model neuron (1907). Brain Res. Bull. 50, 303–304 (1999). https://doi.org/10.1016/S0361-9230(99)00161-6
Stein, R.B.: A theoretical analysis of neuronal variability. Biophys. J. 5(2), 173 (1965)
Rozenberg, M., Schneegans, O., Stoliar, P.: An ultra-compact leaky-integrate-and-fire model for building spiking neural networks. Sci. Rep. 9(1), 1–7 (2019)
Chatterjee, D., Kottantharayil, A.: A CMOS compatible bulk FinFET-based ultra low energy leaky integrate and fire neuron for spiking neural networks. IEEE Electron Device Lett. 40(8), 1301–1304 (2019)
Dutta, S., Kumar, V., Shukla, A., Mohapatra, N.R., Ganguly, U.: Leaky integrate and fire neuron by charge-discharge dynamics in floating-body MOSFET. Sci. Rep. 7(1), 1–7 (2017)
Demirkol, A.Ş., Özoğuz, S.: A low power real time izhikevich neuron with synchronous network behavior. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi. 12(24), 39–52 (2013)
Chua, L.: Memristor-the missing circuit element. IEEE Trans. Circuit Theory. 18(5), 507–519 (1971)
Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The missing memristor found (in English). Nature. 453(7191), 80–83 (2008). https://doi.org/10.1038/nature06932
Williams, S.R.: How we found the missing memristor. Spectrum IEEE. 45(12), 28–35 (2008)
Keshmiri, V.: A Study of the Memristor Models and Applications (2014)
Wong, H.S.P., et al.: Metal-oxide RRAM. Proc. IEEE. 100, 1951–1970 (2012). https://doi.org/10.1109/JPROC.2012.2190369
Strukov, D.B., Borghetti, J.L., Williams, R.S.: Coupled ionic and electronic transport model of thin-film semiconductor memristive behavior (in English). Small. 5(9), 1058–1063 (2009). https://doi.org/10.1002/smll.200801323
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)
Stefanovich, G., Pergament, A., Stefanovich, D.: Electrical switching and Mott transition in VO2. J. Phys. Condens. Matter. 12(41), 8837 (2000)
Honig, J., Reed, T.: Electrical properties of Ti 2 O 3 single crystals. Phys. Rev. 174(3), 1020 (1968)
Chen, J.Y., et al.: Dynamic evolution of conducting nanofilament in resistive switching memories. Nano Lett. 13(8), 3671–3677 (2013). https://doi.org/10.1021/nl4015638
Simmons, J., Verderber, R.: New conduction and reversible memory phenomena in thin insulating films. Proc. R. Soc. London, Ser. A. 301(1464), 77–102 (1967)
Argall, F.: Switching phenomena in titanium oxide thin films. Solid State Electron. 11, 535–541 (1968). https://doi.org/10.1016/0038-1101(68)90092-0
Balanis, C.A.: Advanced Engineering Electromagnetics. John Wiley & Sons, New York (2012)
Swaroop, B., West, W., Martinez, G., Kozicki, M., Akers, L.: Programmable current mode Hebbian learning neural network using programmable metallization cell. In: ISCAS’98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No. 98CH36187), vol. 3, IEEE, pp. 33–36 (1998)
Akopyan, F., et al.: True north: design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip (in English). IEEE Trans. Comput-Aided Des. Integr. Circuits Syst. 34(10), 1537–1557 (2015). https://doi.org/10.1109/tcad.2015.2474396
An, H., Ehsan, M.A., Zhou, Z., Yi, Y.: Electrical Modeling and Analysis of 3D Neuromorphic IC with Monolithic Inter-tier Vias.
Yi, Y., Li, P., Sarin, V., Shi, W.: Impedance extraction for 3-D structures with multiple dielectrics using preconditioned boundary element method. In: 2007 IEEE/ACM International Conference on Computer-Aided Design, IEEE, pp. 7–10 (2007)
Xu, C., Niu, D., Yu, S., Xie, Y.: Modeling and design analysis of 3D vertical resistive memory—a low cost cross-point architecture. In: 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE, pp. 825–830 (2014)
Yi, Y., Li, P., Sarin, V., Shi, W.: A preconditioned hierarchical algorithm for impedance extraction of three-dimensional structures with multiple dielectrics. IEEE Trans. Comput-Aided Des. Integr. Circuits Syst. 27(11), 1918–1927 (2008)
Yi, Y., Zhou, Y., Fu, X., Shen, F.: Modeling differential through-silicon-vias (TSVs) with voltage dependent and nonlinear capacitance. Cyber J. 3(6), 234–241 (2013)
Yang, C.-C., et al.: Footprint-efficient and power-saving monolithic IoT 3D+ IC constructed by BEOL-compatible sub-10nm high aspect ratio (AR>7) single-grained Si FinFETs with record high Ion of 0.38 mA/μm and steep-swing of 65 mV/dec. and I<inf>on</inf>/I<inf>off</inf> ratio of 8," pp. 9.1.1–9.1.4 (2016). https://doi.org/10.1109/iedm.2016.7838379
Shulaker, M.M., et al.: Monolithic 3D integration of logic and memory: Carbon nanotube FETs, resistive RAM, and silicon FETs. In: Electron Devices Meeting (IEDM), 2014 IEEE International, IEEE, pp. 27.4.1–27.4.4 (2014). https://doi.org/10.1109/IEDM.2014.7047120
Shulaker, M.M., et al.: Three-dimensional integration of nanotechnologies for computing and data storage on a single chip. Nature. 547(7661), 74–78 (2017). https://doi.org/10.1038/nature22994
Davies, M., et al.: Advancing neuromorphic computing with Loihi: a survey of results and outlook. Proc. IEEE. 109, 911–934 (2021)
Davies, M., et al.: Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro. 38(1), 82–99 (2018)
Shrestha, S.B., Orchard, G.: Slayer: Spike layer error reassignment in time, arXiv preprint arXiv:1810.08646 (2018)
DiLuoffo, V., Michalson, W.R., Sunar, B.: Robot operating system 2: the need for a holistic security approach to robotic architectures. Int. J. Adv. Robot. Syst. 15(3), 1729881418770011 (2018)
Rao, D., McMahan, B.: Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning, 1st ed. O’Reilly Media, Beijing, p. 1 online resource [Online] (2019). Available: http://proquest.safaribooksonline.com/9781491978221
Bekolay, T., et al.: Nengo: a python tool for building large-scale functional brain models. Front. Neuroinform. 7, 48 (2014)
Goodman, D.F., Brette, R.: The brian simulator. Front. Neurosci. 3, 26 (2009)
Imam, N., Cleland, T.A.: Rapid online learning and robust recall in a neuromorphic olfactory circuit. Nat. Mach. Intell. 2(3), 181–191 (2020)
Moradi, S., Qiao, N., Stefanini, F., Indiveri, G.: A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs). IEEE Trans. Biomed. Circuits Syst. 12(1), 106–122 (2017)
Thakur, C.S., et al.: Large-scale neuromorphic spiking Array processors: a quest to mimic the brain (in English). Front. Neurosci., Review. 12(891) (2018). https://doi.org/10.3389/fnins.2018.00891
Bauer, F.C., Muir, D.R., Indiveri, G.: Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor. IEEE Trans. Biomed. Circuits Syst. 13(6), 1575–1582 (2019)
Sharifshazileh, M., Burelo, K., Sarnthein, J., Indiveri, G.: An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG. Nat. Commun. 12(1), 1–14 (2021)
Akopyan, F., et al.: TrueNorth: design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip. IEEE Trans. Comput-Aided Des. Integr. Circuits Syst. 34(10), 1537–1557 (2015). https://doi.org/10.1109/TCAD.2015.2474396
Benjamin, B., et al.: Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations (in English). Proc. IEEE. 102(5), 699–716 (2014). https://doi.org/10.1109/Jproc.2014.2313565
Models, P., Circuits, N., Project, H.B.: Physical Models of Neural Circuits in BrainScaleS and the Human Brain Project Status and Plans
Meier, K.: A mixed-signal universal neuromorphic computing system. In: 2015 IEEE International Electron Devices Meeting (IEDM), IEEE, pp. 4.6.1–4.6.4 (2015)
Schemmel, J., Bruderle, D., Grubl, A., Hock, M., Meier, K., Millner, S.: A wafer-scale neuromorphic hardware system for large-scale neural modeling. In: Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, IEEE, pp. 1947–1950 (2010)
Appukuttan, S., Bologna, L., Migliore, M., Schürmann, F., Davison, A.: EBRAINS Live Papers-Interactive resource sheets for computational studies in neuroscience (2021)
Markram, H.: The human brain project. Sci. Am. 306(6), 50–55 (2012)
Calimera, A., Macii, E., Poncino, M.: The human brain project and neuromorphic computing. Funct. Neurol. 28, 191–196 (2013). https://doi.org/10.11138/FNeur/2013.28.3.191
Peppicelli, D., et al.: Human Brain Project. Neurorobotics Platform Specification, pp. 1–79 (2015)
Schirner, M., et al.: Brain Modelling as a Service: The Virtual Brain on EBRAINS, arXiv preprint arXiv:2102.05888 (2021)
Andrychowicz, O.M., et al.: Learning dexterous in-hand manipulation. Int. J. Robot. Res. 39(1), 3–20 (2020)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate, arXiv preprint arXiv:1409.0473 (2014)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805 (2018)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning. MIT Press, Cambridge, MA (2016)
Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2017-Octob, pp. 843–852 (2017). https://doi.org/10.1109/ICCV.2017.97
Deng, L., Tang, H., Roy, K.: Understanding and bridging the gap between neuromorphic computing and machine learning. Front. Comput. Neurosci. 15 (2021)
Roy, K., Jaiswal, A., Panda, P.: Towards spike-based machine intelligence with neuromorphic computing. Nature. 575(7784), 607–617 (2019)
Arthur, I.J., Dada, P.: Algorithm Prototyping, Development, and Deployment for TrueNorth: The Caffe Tea Case Study (2015)
Sze, V., Chen, Y.-H., Yang, T.-J., Emer, J.S.: Efficient processing of deep neural networks: a tutorial and survey. Proc. IEEE. 105(12), 2295–2329 (2017)
An, H., Zhou, Z., Yi, Y.: Opportunities and challenges on nanoscale 3D neuromorphic computing system. In: Electromagnetic Compatibility & Signal/Power Integrity (EMCSI), 2017 IEEE International Symposium on, IEEE, pp. 416–421 (2017)
Severa, W., Vineyard, C.M., Dellana, R., Verzi, S.J., Aimone, J.B.: Training deep neural networks for binary communication with the Whetstone method. Nat. Mach. Intell. 1(2), 86 (2019)
Drazen, D., Lichtsteiner, P., Häfliger, P., Delbrück, T., Jensen, A.: Toward real-time particle tracking using an event-based dynamic vision sensor. Exp. Fluids. 51(5), 1465 (2011)
Delbruck, T., Lang, M.: Robotic goalie with 3 ms reaction time at 4% CPU load using event-based dynamic vision sensor. Front. Neurosci. 7, 223 (2013)
Blum, H., Dietmüller, A., Milde, M., Conradt, J., Indiveri, G., Sandamirskaya, Y.: A neuromorphic controller for a robotic vehicle equipped with a dynamic vision sensor. Robot. Sci. Syst. 2017 (2017)
Dominguez-Morales, M.J., Jimenez-Fernandez, A., Jiménez-Moreno, G., Conde, C., Cabello, E., Linares-Barranco, A.: Bio-inspired stereo vision calibration for dynamic vision sensors. IEEE Access. 7, 138415–138425 (2019)
Choi, S.-Y., Kim, J.-S., Seo, J.-H.: A study on the reduction of power consumption and the improvement of motion blur for OLED displays. J. Korean Inst. IIIum. Electr. Install. Eng. 30(3), 1–8 (2016)
Chen, G., et al.: Neuromorphic vision based multivehicle detection and tracking for intelligent transportation system. J. Adv. Transport. 2018, 4815383 (2018)
Anumula, J., Neil, D., Delbruck, T., Liu, S.-C.: Feature representations for neuromorphic audio spike streams. Front. Neurosci. 12, 23 (2018)
Liu, S.C., Delbruck, T.: Neuromorphic sensory systems. Curr. Opin. Neurobiol. 20, 288–295 (2010). https://doi.org/10.1016/j.conb.2010.03.007
Richter, C., et al.: Musculoskeletal robots: scalability in neural control. IEEE Robot. Autom. Mag. 23, 128–137 (2016). https://doi.org/10.1109/MRA.2016.2535081
Vanarse, A., Osseiran, A., Rassau, A.: A review of current neuromorphic approaches for vision, auditory, and olfactory sensors. Front. Neurosci. 10, 115 (2016)
Sheng, M., Sabatini, B.L., Südhof, T.C.: Synapses and Alzheimer’s disease. Cold Spring Harb. Perspect. Biol. 4(5), a005777 (2012)
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Zins, N., Zhang, Y., Yu, C., An, H. (2023). Neuromorphic Computing: A Path to Artificial Intelligence Through Emulating Human Brains. In: Iranmanesh, A. (eds) Frontiers of Quality Electronic Design (QED). Springer, Cham. https://doi.org/10.1007/978-3-031-16344-9_7
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