Fetching the paper…
Reading the bibliography…
Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the potential of enabling ultra-low power pervasive sensory processing.
B. Widrow and M. Hoff, “Adaptive Switching Circuits,” in 1960 IRE WESCON Convention Record, Part 4 . New York: IRE, 1960, pp. 96–104. [Online]. Available: http://isl-www.stanford.edu/˜widrow/papers/c1960adaptiveswitching.pdf
1960
Earlier work this paper cites.
J. Backus, “Can programming be liberated from the von Neumann style?: a functional style and its algebra of programs,” Communications of the ACM , vol. 21, no. 8, pp. 613–641, 1978. [Online]. Available: http://doi.acm.org/10.1145/359576.359579
1978
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hintont, and R. J. Williams, “Learning representations by back-propagating errors,” Nature , vol. 323, no. 6088, pp. 533–536, 1986
1986
Earlier work this paper cites.
C. Mead, “Neuromorphic electronic systems,” Proceedings of the IEEE , vol. 78, no. 10, pp. 1629–36, 1990
1990
Earlier work this paper cites.
T. Delbrueck and C. Mead, “Bump circuits,” in Proceedings of International Joint Conference on Neural Networks , vol. 1, 1993, pp. 475–479
1993
Earlier work this paper cites.
S.-C. Liu, J. Kramer, G. Indiveri, T. Delbruck, and R. Douglas, Analog VLSI:Circuits and Principles . MIT Press, 2002
2002
Earlier work this paper cites.
A. Pirovano, A. L. Lacaita, F. Pellizzer, S. A. Kostylev, A. Benvenuti, and R. Bez, “Low-field amorphous state resistance and threshold voltage drift in chalcogenide materials,” IEEE Transactions on Electron Devices , vol. 51, no. 5, pp. 714–719, 2004
2004
Earlier work this paper cites.
C. Bartolozzi and G. Indiveri, “Synaptic dynamics in analog VLSI,” Neural Computation , vol. 19, no. 10, pp. 2581–2603, Oct 2007
2007
Earlier work this paper cites.
I. Karpov, M. Mitra, D. Kau, G. Spadini, Y. Kryukov, and V. Karpov, “Fundamental drift of parameters in chalcogenide phase change memory,” Journal of Applied Physics , vol. 102, no. 12, p. 124503, 2007
2007
Earlier work this paper cites.
J. M. Brader, W. Senn, and S. Fusi, “Learning real-world stimuli in a neural network with spike-driven synaptic dynamics,” Neural Computation , vol. 19, no. 11, pp. 2881–2912, 2007
2007
Earlier work this paper cites.
D. Ielmini, S. Lavizzari, D. Sharma, and A. L. Lacaita, “Temperature acceleration of structural relaxation in amorphous ge2sb2te5,” Applied Physics Letters , vol. 92, no. 19, p. 193511, 2008
2008
Earlier work this paper cites.
G. Indiveri and T. Horiuchi, “Frontiers in neuromorphic engineering,” Frontiers in Neuroscience , vol. 5, no. 118, pp. 1–2, 2011
2011
Earlier work this paper cites.
S. Kim, B. Lee, M. Asheghi, F. Hurkx, J. P. Reifenberg, K. E. Goodson, and H.-S. P. Wong, “Resistance and threshold switching voltage drift behavior in phase-change memory and their temperature dependence at microsecond time scales studied using a micro-thermal stage,” IEEE Transactions on Electron Devices , vol. 58, no. 3, pp. 584–592, 2011
2011
Earlier work this paper cites.
M. Sanhueza and J. Lisman, “The camkii/nmdar complex as a molecular memory,” Molecular brain , vol. 6, no. 1, pp. 1–8, 2013
2013
Earlier work this paper cites.
E. Chicca, F. Stefanini, C. Bartolozzi, and G. Indiveri, “Neuromorphic electronic circuits for building autonomous cognitive systems,” Proceedings of the IEEE , vol. 102, no. 9, pp. 1367–1388, 9 2014
2014
Cited alongside, same era.
S. Furber, F. Galluppi, S. Temple, and L. Plana, “The SpiNNaker project,” Proceedings of the IEEE , vol. 102, no. 5, pp. 652–665, May 2014
2014
Cited alongside, same era.
N. Qiao, H. Mostafa, F. Corradi, M. Osswald, F. Stefanini, D. Sumislawska, and G. Indiveri, “A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128k synapses,” Frontiers in neuroscience , vol. 9, p. 141, 2015
2015
Cited alongside, same era.
G. Indiveri and S.-C. Liu, “Memory and information processing in neuromorphic systems,” Proceedings of the IEEE , vol. 103, no. 8, pp. 1379–1397, 2015
2015
Cited alongside, same era.
I. Boybat, M. L. Gallo, T. Moraitis, T. Parnell, T. Tuma, B. Rajendran, Y. Leblebici, A. Sebastian, E. Eleftheriou et al. , “Neuromorphic computing with multi-memristive synapses,” Nature communications , vol. 9, p. 2514, 2018
2018
Later among the works it cites.
C. Frenkel, M. Lefebvre, J.-D. Legat, and D. Bol, “A 0.086-mm2 12.7-pj/SOP 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm CMOS,” IEEE Transactions on Biomedical Circuits and Systems , vol. 13, no. 1, pp. 145–158, 2019
2019
Later among the works it cites.
M. Payvand and G. Indiveri, “Spike-based plasticity circuits for always-on on-line learning in neuromorphic systems,” in 2019 IEEE International Symposium on Circuits and Systems (ISCAS) . IEEE, 2019, pp. 1–5
2019
Later among the works it cites.
A. Rubino, M. Payvand, and G. Indiveri, “Ultra-low power silicon neuron circuit for extreme-edge neuromorphic intelligence,” in International Conference on Electronics, Circuits, and Systems, (ICECS), 2019 , 11 2019, pp. 458–461
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Serb, J. Bill, A. Khiat, R. Berdan, R. Legenstein, and T. Prodromakis, “Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses,” Nature communications , vol. 7, p. 12611, 2016
2016
Cited alongside, same era.
N. Qiao, C. Bartolozzi, and G. Indiveri, “An ultralow leakage synaptic scaling homeostatic plasticity circuit with configurable time scales up to 100 ks,” IEEE Transactions on Biomedical Circuits and Systems , 2017
2017
Cited alongside, same era.
Y. Li, Z. Wang, R. Midya, Q. Xia, and J. J. Yang, “Review of memristor devices in neuromorphic computing: materials sciences and device challenges,” Journal of Physics D: Applied Physics , vol. 51, no. 50, p. 503002, 2018
2018
Cited alongside, same era.
W. Gerstner, M. Lehmann, V. Liakoni et al. , “Eligibility traces and plasticity on behavioral time scales: experimental support of neohebbian three-factor learning rules,” Front. Neur. Circ. , vol. 12, p. 53, 2018
2018
Cited alongside, same era.
E. O. Neftci, “Data and power efficient intelligence with neuromorphic learning machines,” iScience , vol. 5, pp. 52–68, 2018
2018
Cited alongside, same era.
F. Zenke and S. Ganguli, “Superspike: Supervised learning in multilayer spiking neural networks,” Neural computation , vol. 30, no. 6, pp. 1514–1541, 2018
2018
Cited alongside, same era.
M. Davies, N. Srinivasa, T.-H. Lin, G. Chinya, Y. Cao, S. H. Choday, G. Dimou, P. Joshi, N. Imam, S. Jain et al. , “Loihi: A neuromorphic manycore processor with on-chip learning,” IEEE Micro , vol. 38, no. 1, pp. 82–99, 2018
2018
Cited alongside, same era.
M. Le Gallo, D. Krebs, F. Zipoli, M. Salinga, and A. Sebastian, “Collective structural relaxation in phase-change memory devices,” Advanced Electronic Materials , vol. 4, no. 9, p. 1700627, 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
M. P. Lehmann, H. A. Xu, V. Liakoni, M. H. Herzog, W. Gerstner, and K. Preuschoff, “One-shot learning and behavioral eligibility traces in sequential decision making,” Elife , vol. 8, p. e47463, 2019
2019
Later among the works it cites.
M. Payvand, M. V. Nair, L. K. Müller, and G. Indiveri, “A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: From mitigation to exploitation,” Faraday Discussions , vol. 213, pp. 487–510, 2019
2019
Later among the works it cites.
C. Mead, “How we created neuromorphic engineering,” Nature Electronics , vol. 3, no. 7, pp. 434–435, 2020
2020
Later among the works it cites.
E. Chicca and G. Indiveri, “A recipe for creating ideal hybrid memristive-CMOS neuromorphic processing systems,” Applied Physics Letters , vol. 116, no. 12, p. 120501, 2020
2020
Later among the works it cites.
S. Spiga, A. Sebastian, D. Querlioz, and B. Rajendran, “Role of resistive memory devices in brain-inspired computing,” in Memristive Devices for Brain-Inspired Computing , ser. Woodhead Publishing Series in Electronic and Optical Materials, S. Spiga, A. Sebastian, D. Querlioz, and B. Rajendran, Eds. Woodhead Publishing, 2020, pp. 3–16
2020
Later among the works it cites.
M. Payvand, Y. Demirag, T. Dalgaty, E. Vianello, and G. Indiveri, “Analog weight updates with compliance current modulation of binary rerams for on-chip learning,” in 2020 IEEE International Symposium on Circuits and Systems (ISCAS) . IEEE, 2020, pp. 1–5
2020
Later among the works it cites.
G. Bellec, F. Scherr, A. Subramoney, E. Hajek, D. Salaj, R. Legenstein, and W. Maass, “A solution to the learning dilemma for recurrent networks of spiking neurons,” bioRxiv , p. 738385, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
M. Payvand, M. E. Fouda, F. Kurdahi, A. Eltawil, and E. O. Neftci, “Error-triggered three-factor learning dynamics for crossbar arrays,” in 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) . IEEE, 2020, pp. 218–222
2020
Later among the works it cites.
A. Payeur, J. Guerguiev, F. Zenke, B. A. Richards, and R. Naud, “Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits,” bioRxiv , 2020
2020
Later among the works it cites.