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The biologically inspired spiking neurons used in neuromorphic computing are nonlinear filters with dynamic state variables -- very different from the stateless neuron models used in deep learning.
E. H. Adelson and J. R. Bergen, “Spatiotemporal energy models for the perception of motion,” J. Optical Society of America , vol. 2, no. 2, pp. 284–299, 1985
1985
Earlier work this paper cites.
V. M. Eguíluz, M. Ospeck, Y. Choe, A. Hudspeth, and M. O. Magnasco, “Essential nonlinearities in hearing,” Phys. Rev. Lett. , vol. 84, no. 22, pp. 5232–5235, 2000
2000
Earlier work this paper cites.
E. M. Izhikevich, “Resonate-and-fire neurons,” Neural Netw. , vol. 14, no. 6, pp. 883–894, 2001
2001
Earlier work this paper cites.
A. Kern and R. Stoop, “Essential role of couplings between hearing nonlinearities,” Phys. Rev. Lett. , vol. 91, no. 12, p. 128101, 2003
2003
Earlier work this paper cites.
J. Tapson, T. J. Hamilton, C. Jin, and A. van Schaik, “Self-tuned regenerative amplification and the hopf bifurcation,” in IEEE Int. Symp. Circuits Syst. (ISCAS) , 2008, pp. 1768–1771
2008
Earlier work this paper cites.
J. V. Arthur and K. A. Boahen, “Silicon-neuron design: A dynamical systems approach,” IEEE Trans. Circuits Syst. I , vol. 58, no. 5, pp. 1034–1043, 2010
2010
Earlier work this paper cites.
M. S. Zilany, I. C. Bruce, and L. H. Carney, “Updated parameters and expanded simulation options for a model of the auditory periphery,” The Journal of the Acoustical Society of America , vol. 135, no. 1, pp. 283–286, 2014
2014
Earlier work this paper cites.
M. Davies, N. Srinivasa, T. Lin, G. Chinya, Y. Cao, S. H. Choday, G. Dimou, P. Joshi, N. Imam, S. Jain, Y. Liao, C. Lin, A. Lines, R. Liu, D. Mathaikutty, S. McCoy, A. Paul, J. Tse, G. Venkataramanan, Y. Weng, A. Wild, Y. Yang, and H. Wang, “Loihi: A Neuromorphic Manycore Processor with On-Chip Learning,” IEEE Micro , vol. 38, no. 1, pp. 82–99, 2018
2018
Cited alongside, same era.
A. Zihao Zhu, D. Thakur, T. Özaslan, B. Pfrommer, V. Kumar, and K. Daniilidis, “The Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception,” IEEE Robot. Autom. Lett. , vol. 3, no. 3, pp. 2032–2039, 2018
2018
Cited alongside, same era.
S. B. Shrestha and G. Orchard, “SLAYER: Spike layer error reassignment in time,” in Conf. Neural Inf. Process. Syst. (NIPS) , 2018, pp. 1412–1421
2018
Cited alongside, same era.
J. Anumula, D. Neil, T. Delbruck, and S.-C. Liu, “Feature representations for neuromorphic audio spike streams,” Front. Neurosci. , vol. 12, 2018
2018
G. Gallego, T. Delbruck, G. M. Orchard, C. Bartolozzi, B. Taba, A. Censi, S. Leutenegger, A. Davison, J. Conradt, K. Daniilidis, and D. Scaramuzza, “Event-based Vision: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell. , 2020
2020
Later among the works it cites.
O. Rybakov, N. Kononenko, N. Subrahmanya, M. Visontai, and S. Laurenzo, “Streaming keyword spotting on mobile devices,” in Interspeech , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
M. Davies, A. Wild, G. Orchard, Y. Sandamirskaya, G. A. F. Guerra, P. Joshi, P. Plank, and S. R. Risbud, “Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook,” Proc. IEEE , vol. 109, no. 5, pp. 911–934, 2021
2021
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Cited alongside, same era.
2018
Cited alongside, same era.
E. P. Frady and F. T. Sommer, “Robust computation with rhythmic spike patterns,” PNAS , vol. 116, no. 36, pp. 18 050–18 059, 2019
2019
Cited alongside, same era.
F. Peveri, S. Testa, and S. P. Sabatini, “A Cortically-Inspired Architecture for Event-Based Visual Motion Processing: From Design Principles to Real-World Applications,” in IEEE Conf. Comput. Vis. Pattern Recog. Workshops (CVPRW) , 2021, pp. 1395–1402
2021
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