372
9 Memristor Cellular Neural Networks Computing in the Flux-charge Domain
7. O. Krestinskaya, A.P. James, L.O. Chua, Neuromemristive circuits for edge computing: a
review. IEEE Trans. Neural Netw. Learn. Syst. 31(1), 4–23 (2020). https://doi.org/10.1109/
TNNLS.2019.2899262
8. L.O. Chua, L. Yang, Cellular neural networks: theory. IEEE Trans. Circuits Syst. 35(10), 1257–
1272 (1988)
9. L.O. Chua, T. Roska, Cellular Neural Networks and Visual Computing: Foundation and
Applications (Cambridge University Press, Cambridge, 2005)
10. D.B. Strukov, G.S. Snider, D.R. Stewart, R.S. Williams, The missing memristor found. Nature
453(7191), 80–83 (2008)
11. Y.N. Joglekar, S.J. Wolf, The elusive memristor: properties of basic electrical circuits. Eur. J.
Phys. 30(4), 661 (2009)
12. R. Stanley Williams, How we found the missing memristor. IEEE Spectr. 45(12), 28–35 (2008)
13. F. Corinto, A. Ascoli, A boundary condition-based approach to the modeling of memristor
nanostructures. IEEE Trans. Circuits Syst. I. Regul. Pap. 59(11), 2713–2726 (2012)
14. D. Biolek, Z. Biolek, V. Biolkova, Z. Kolka, Reliable modeling of ideal generic memristors via
state-space transformation. Radioengineering 24(2), 393–407 (2015)
15. L.O. Chua, L. Yang, Cellular neural networks: applications. IEEE Trans. Circuits Syst. 35(10),
1273–1290 (1988)
16. M. Hirsch, Convergent activation dynamics in continuous time networks. Neural Netw. 2, 331–
349 (1989)
17. H.K. Khalil, Nonlinear Systems (Prentice Hall, Upper Saddle River, 2002)
18. J.K. Hale, Ordinary Differential Equations (Wiley Interscience, New York, 1969)
19. M. Di Marco, M. Forti, L. Pancioni, Complete stability of feedback CNNs with dynamic
memristors and second-order cells. Int. J. Circuit Theory Appl. 44, 1959–1981 (2016)
20. L. Kék, K. Karacs, T. Roska, Cellular wave computing library (templates, algorithms,
and programs). Version 2.1, in Research report of Cellular Sensory and Wave Computing
Laboratory CSW-1-2007 (2007)
21. M. Di Marco, M. Forti, L. Pancioni, Convergence and multistability of nonsymmetric cellular
neural networks with memristors. IEEE Trans. Cybern. 47(10), 2970–2983 (2017)
22. M. Di Marco, M. Forti, L. Pancioni, New conditions for global asymptotic stability of
memristor neural networks. IEEE Trans. Neural Netw. Learn. Syst. 29, 1822–1834 (2018)
23. M. Di Marco, M. Forti, L. Pancioni, Stability of memristor neural networks with delays
operating in the flux-charge domain. J. Franklin Inst. 355(12), 5135–5162 (2018)
24. R. Tetzlaff, A. Ascoli, I. Messaris, L.O. Chua, Theoretical foundations of memristor cellular
nonlinear networks: memcomputing with bistable-like memristors. IEEE Trans. Circuits Syst.
I. Regul. Pap. 67(2), 502–515 (2020)
25. A. Ascoli, I. Messaris, R. Tetzlaff, L.O. Chua, Theoretical foundations of memristor cellular
nonlinear networks: stability analysis with dynamic memristors. IEEE Trans. Circuits Syst. I:
Regul. Pap. 67(4), 1389–1401 (2020)
26. A. Ascoli, R. Tetzlaff, S.-M. Kang, L.O. Chua, Theoretical foundations of memristor cellular
nonlinear networks: a DRM2-based method to design memcomputers with dynamic memristors. IEEE Trans. Circuits Syst. I: Regul. Pap. 67(8), 2753–2766 (2020). https://doi.org/10.
1109/TCSI.2020.2978460
27. Y.V. Pershin, S. La Fontaine, M. Di Ventra, Memristive model of amoeba learning. Phys. Rev.
E 80(2), 021926 (2009)
28. T. Roska, L.O. Chua, The CNN universal machine: an analogic array computer. IEEE Trans.
Circuits Syst. II. Analog Digit. Signal Process. 40(3):163–173 (1993)
9 Memristor Cellular Neural Networks Computing in the Flux-charge Domain
7. O. Krestinskaya, A.P. James, L.O. Chua, Neuromemristive circuits for edge computing: a
review. IEEE Trans. Neural Netw. Learn. Syst. 31(1), 4–23 (2020). https://doi.org/10.1109/
TNNLS.2019.2899262
8. L.O. Chua, L. Yang, Cellular neural networks: theory. IEEE Trans. Circuits Syst. 35(10), 1257–
1272 (1988)
9. L.O. Chua, T. Roska, Cellular Neural Networks and Visual Computing: Foundation and
Applications (Cambridge University Press, Cambridge, 2005)
10. D.B. Strukov, G.S. Snider, D.R. Stewart, R.S. Williams, The missing memristor found. Nature
453(7191), 80–83 (2008)
11. Y.N. Joglekar, S.J. Wolf, The elusive memristor: properties of basic electrical circuits. Eur. J.
Phys. 30(4), 661 (2009)
12. R. Stanley Williams, How we found the missing memristor. IEEE Spectr. 45(12), 28–35 (2008)
13. F. Corinto, A. Ascoli, A boundary condition-based approach to the modeling of memristor
nanostructures. IEEE Trans. Circuits Syst. I. Regul. Pap. 59(11), 2713–2726 (2012)
14. D. Biolek, Z. Biolek, V. Biolkova, Z. Kolka, Reliable modeling of ideal generic memristors via
state-space transformation. Radioengineering 24(2), 393–407 (2015)
15. L.O. Chua, L. Yang, Cellular neural networks: applications. IEEE Trans. Circuits Syst. 35(10),
1273–1290 (1988)
16. M. Hirsch, Convergent activation dynamics in continuous time networks. Neural Netw. 2, 331–
349 (1989)
17. H.K. Khalil, Nonlinear Systems (Prentice Hall, Upper Saddle River, 2002)
18. J.K. Hale, Ordinary Differential Equations (Wiley Interscience, New York, 1969)
19. M. Di Marco, M. Forti, L. Pancioni, Complete stability of feedback CNNs with dynamic
memristors and second-order cells. Int. J. Circuit Theory Appl. 44, 1959–1981 (2016)
20. L. Kék, K. Karacs, T. Roska, Cellular wave computing library (templates, algorithms,
and programs). Version 2.1, in Research report of Cellular Sensory and Wave Computing
Laboratory CSW-1-2007 (2007)
21. M. Di Marco, M. Forti, L. Pancioni, Convergence and multistability of nonsymmetric cellular
neural networks with memristors. IEEE Trans. Cybern. 47(10), 2970–2983 (2017)
22. M. Di Marco, M. Forti, L. Pancioni, New conditions for global asymptotic stability of
memristor neural networks. IEEE Trans. Neural Netw. Learn. Syst. 29, 1822–1834 (2018)
23. M. Di Marco, M. Forti, L. Pancioni, Stability of memristor neural networks with delays
operating in the flux-charge domain. J. Franklin Inst. 355(12), 5135–5162 (2018)
24. R. Tetzlaff, A. Ascoli, I. Messaris, L.O. Chua, Theoretical foundations of memristor cellular
nonlinear networks: memcomputing with bistable-like memristors. IEEE Trans. Circuits Syst.
I. Regul. Pap. 67(2), 502–515 (2020)
25. A. Ascoli, I. Messaris, R. Tetzlaff, L.O. Chua, Theoretical foundations of memristor cellular
nonlinear networks: stability analysis with dynamic memristors. IEEE Trans. Circuits Syst. I:
Regul. Pap. 67(4), 1389–1401 (2020)
26. A. Ascoli, R. Tetzlaff, S.-M. Kang, L.O. Chua, Theoretical foundations of memristor cellular
nonlinear networks: a DRM2-based method to design memcomputers with dynamic memristors. IEEE Trans. Circuits Syst. I: Regul. Pap. 67(8), 2753–2766 (2020). https://doi.org/10.
1109/TCSI.2020.2978460
27. Y.V. Pershin, S. La Fontaine, M. Di Ventra, Memristive model of amoeba learning. Phys. Rev.
E 80(2), 021926 (2009)
28. T. Roska, L.O. Chua, The CNN universal machine: an analogic array computer. IEEE Trans.
Circuits Syst. II. Analog Digit. Signal Process. 40(3):163–173 (1993)