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1 1 CS231n Convolutional Neural Networks for Visual Recognition. Stanford University. https://cs231n.github.io/neural‐networks‐1

2 2 Haykin, S. (1996). Adaptive Filter Theory, 3e. Upper Saddle River, NJ: Prentice‐Hall.

3 3 Haykin, S. (1996). Neural networks expand SP's horizons. IEEE Signal Process. Mag. 13 (2): 24–49.

4 4 Haykin, S. (1999). Neural Networks: A Comprehensive Foundation, 2e. Upper Saddle River, NJ: Prentice‐Hall.

5 5 Wan, E.A. (1993). Finite impulse response neural networks with applications in time series prediction, Ph.D. dissertation. Department of Electrical Engineering, Stanford University, Stanford, CA.

6 6 Box, G. and Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. San Francisco, CA: Holden‐Day.

7 7 Weigend, A.S. and Gershenfeld, N.A. (1994). Time Series Prediction: Fore‐ Casting the Future and Understanding the Past. Reading, MA: Addison‐Wesley.

8 8 Hochreiter, S. and Schmidhuber, J. (1997). Long short‐term memory. Neural Comput. 9 (8): 1735–1780.

9 9 Schuster, M. and Paliwal, K.K. (1997). Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45 (11): 2673–2681.

10 10 Graves, A. and Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18 (5): 602–610.

11 11 Graves, A., Liwicki, M., Fernandez, S. et al. (2009). A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31 (5): 855–868.

12 12 Graves, A., Wayne, G., and Danihelka, I. (2014). Neural Turing machines. arXiv preprint arXiv:1410.5401.

13 13 Baddeley, A., Sala, S.D., and Robbins, T.W. (1996). Working memory and executive control [and discussion]. Philos. Trans. R. Soc. B: Biol. Sci. 351 (1346): 1397–1404.

14 14 Chua, L.O. and Roska, T. (2002). Cellular Neural Networks and Visual Computing: Foundations and Applications. New York, NY: Cambridge University Press.

15 15 Chua, L.O. and Yang, L. (1988). Cellular neural network: theory. IEEE Trans. Circuits Syst. 35: 1257–1272.

16 16 Molinar‐Solis, J.E., Gomez‐Castaneda, F., Moreno, J. et al. (2007). Programmable CMOS CNN cell based on floating‐gate inverter unit. J. VLSI Signal Process. Syst. Signal, Image, Video Technol. 49: 207–216.

17 17 Pan, C. and Naeemi, A. (2016). A proposal for energy‐efficient cellular neural network based on spintronic devices. IEEE Trans. Nanotechnol. 15 (5): 820–827.

18 18 Wang, L. et al. (1998). Time multiplexed color image processing based on a CNN with cell‐state outputs. IEEE Trans. VLSI Syst. 6 (2): 314–322.

19 19 Roska, T. and Chua, L.O. (1993). The CNN universal machine: an analogic array computer. IEEE Trans. Circuits Systems II: Analog Digital Signal Process. 40 (3): 163–173.

20 20 Pickett, M.D. et al. (2009). Switching dynamics in titanium dioxide memristive devices. J. Appl. Phys. 106 (7): 074508.

21 21 Hu, X., Duan, S., and Wang, L. (2012). A novel chaotic neural network using memristive synapse with applications in associative memory. Abstract Appl. Anal. 2012: 1–19. https://doi.org/10.1155/2012/405739.

22 22 Kim, H. et al. (2012). Memristor bridge synapses. Proc. IEEE 100 (6): 2061–2070.

23 23 Adhikari, S.P., Yang, C., Kim, H., and Chua, L.O. (2012). Memristor bridge synapse‐based neural network and its learning. IEEE Trans. Neural Netw. Learn. Syst. 23 (9): 1426–1435.

24 24 Corinto, F., Ascoli, A., Kim, Y.‐S., and Min, K.‐S. (2014). Cellular nonlinear networks with memristor synapses. In: Memristor Networks, ed. Andrew Adamatzky, Leon Chua 267–291. New York, NY: Springer‐Verlag.

25 25 Wang, L., Drakakis, E., Duan, S., and He, P. (2012). Memristor model and its application for chaos generation. Int. J. Bifurcation Chaos 22 (8): 1250205.

26 26 Liu, S., Wang, L., Duan, S. et al. (2012). Memristive device based filter and integration circuits with applications. Adv. Sci. Lett. 8 (1): 194–199.

27 27 Chua, L.O. and Kang, S.M. (1976). Memristive devices and systems. Proc. IEEE 64 (2): 209–223.

28 28 Kvatinsky, S., Friedman, E.G., Kolodny, A., and Weiser, U.C. (2013). TEAM: threshold adaptive memristor model. IEEE Trans. Circuits Syst. I, Reg. Papers 60 (1): 211–221.

29 29 Strachan, J. et al. (2013). State dynamics and modeling of tantalum oxide memristors. IEEE Trans. Electron Devices 60 (7): 2194–2202.

30 30 Saha, S. A Comprehensive Guide to Convolutional Neural Networks. https://towardsdatascience.com/a‐comprehensive‐guide‐to‐convolutional‐neural‐networks‐the‐eli5‐way‐3bd2b1164a53