[1] Liu, F., et al., A New Fuzzy Spiking Neural Network Based on Neuronal Contribution Degree. IEEE Transactions on Fuzzy Systems, 2021.
[2] Qasem, S.N. and A. Mohammadzadeh, A deep learned type-2 fuzzy neural network: Singular value decomposition approach. Applied Soft Computing, 2021. 105: p. 107244.
[3] Hebb, D.O., The organization of behavior: A neuropsychological theory. 2005: Psychology Press.
[4] Fox, M.D. and M.E. Raichle, Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature reviews neuroscience, 2007. 8(9): p. 700-711.
[5] Abbott, L.F. and S.B. Nelson, Synaptic plasticity: taming the beast. Nature neuroscience, 2000. 3(11): p. 1178-1183.
[6] Pan, H.-j., et al. A Category Theory Model for Learning and Memory of the Human Brain. in 2010 International Conference on Digital Manufacturing & Automation. 2010. IEEE.
[7] French, R.M., Catastrophic forgetting in connectionist networks. Trends in cognitive sciences, 1999. 3(4): p. 128-135.
[8] Averkin, A. and S. Yarushev, Review of research in the field of developing methods to extract rules from artificial neural networks. Journal of Computer and Systems Sciences International, 2021. 60(6): p. 966-980.
[9] Nakayama, H. and K. Yoshii. Active forgetting in machine learning and its application to financial problems. in Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. 2000. IEEE.
[10] Salganicoff, M., Explicit Forgetting Algorithms for Memory Based Learning. 1993.
[11] Panda, P., et al., Asp: Learning to forget with adaptive synaptic plasticity in spiking neural networks. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2017. 8(1): p. 51-64.
[12] Auge, D., et al., A survey of encoding techniques for signal processing in spiking neural networks. Neural Processing Letters, 2021. 53(6): p. 4693-4710.
[13] Kato, A. and K. Morita, Forgetting in reinforcement learning links sustained dopamine signals to motivation. PLoS computational biology, 2016. 12(10): p. e1005145.
[14] Shouraki, S.B. and N. Honda. Fuzzy controller design by an active learning method. in 31th symposium of intelligent control. Tokyo, Japan. 1998.
[15] Merrikh-Bayat, F., S.B. Shouraki, and A. Rohani, Memristor crossbar-based hardware implementation of the IDS method. IEEE Transactions on Fuzzy Systems, 2011. 19(6): p. 1083-1096.
[16] Afrakoti, I.E.P., S.B. Shouraki, and B. Haghighat, An optimal hardware implementation for active learning method based on memristor crossbar structures. IEEE Systems Journal, 2014. 8(4): p. 1190-1199.
[17] Shahdi, S.A. and S.B. Shouraki. Supervised active learning method as an intelligent linguistic controller and its hardware implementation. in 2nd IASTEAD International Conference on Artificial Intelligence and Applications (AIA'02), Malaga, Spain. 2002.
[18] Klidbary, S.H., et al. Outlier robust fuzzy active learning method (ALM). in 2017 7th international conference on computer and knowledge engineering (ICCKE). 2017. IEEE.
[19] Klidbary, S.H., S.B. Shouraki, and I.E.P. Afrakoti, An adaptive efficient memristive ink drop spread (IDS) computing system. Neural Computing and Applications, 2019. 31(11): p. 7733-7754.
[20] Jokar, E., et al., Hardware-Algorithm Co-Design of a Compressed Fuzzy Active Learning Method. IEEE Transactions on Circuits and Systems I: Regular Papers, 2020. 67(12): p. 4932-4945.
[21] Klidbary, S.H. and S.B. Shouraki, A novel adaptive learning algorithm for low-dimensional feature space using memristor-crossbar implementation and on-chip training. Applied Intelligence, 2018. 48(11): p. 4174-4191.
[22] Javadian, M., et al., Refining membership degrees obtained from fuzzy C-means by re-fuzzification. Iranian Journal of Fuzzy Systems, 2020. 17(4): p. 85-104.
[23] سجاد حق زاد کلیدبری، سعید باقری شورکی، ارائه اپراتور جدید جایگزین پخش قطره جوهر در روش یادگیری فعال. مجله مهندسی برق دانشگاه تبریز, 2019.
[24] Klidbary, S.H., S.B. Shouraki, and B. Linares-Barranco, Digital hardware realization of a novel adaptive ink drop spread operator and its application in modeling and classification and on-chip training. International Journal of Machine Learning and Cybernetics, 2019. 10(9): p. 2541-2561.
[25] Javadian, M., A. Hejazi, and S.H. Klidbary, Obtaining Fuzzy Membership Function of Clusters With the Memristor Hardware Implementation and On-Chip Learning. IEEE Transactions on Emerging Topics in Computational Intelligence, 2022.
[26] Murakami, M. and N. Honda, A study on the modeling ability of the IDS method: A soft computing technique using pattern-based information processing. International journal of approximate reasoning, 2007. 45(3): p. 470-487.
[27] Murakami, M. and N. Honda. Classification performance of the IDS method based on the two-spiral benchmark. in 2005 IEEE International Conference on Systems, Man and Cybernetics. 2005. IEEE.
[28] Hwang, J.-N., et al., Regression modeling in back-propagation and projection pursuit learning. IEEE Transactions on neural networks, 1994. 5(3): p. 342-353.