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Theoretical Foundations for the Alpha-Beta Associative Memories: 10 Years of Derived Extensions, Models, and Applications

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Abstract

The current paper contains the theoretical foundation for the off-the-mainstream model known as Alpha-Beta associative memories (\(\alpha \beta \) model). This is an unconventional computation model designed to operate as an associative memory, whose main application is the solution of pattern recognition tasks, particularly for pattern recall and pattern classification. Although this model was devised, proposed and created in 2002, it is worth noting that its theoretical support remains unpublished to this day. This is despite the fact that more than a hundred scientific articles have been published with applications, improvements, and new models derived from the \(\alpha \beta \) model. The present paper includes all the required definitions, and the rigorous mathematical demonstrations of the lemmas and theorems, explaining the operation of the \(\alpha \beta \) model, as well as the original models it has inspired or that have been derived from it. Also, brief descriptions of 60 selected articles related to the \(\alpha \beta \) model are presented. These latter works illustrate the competitiveness (and sometimes superiority) of several extensions and models derived from the original \(\alpha \beta \) model, when compared against some models and paradigms present in the mainstream current scientific literature.

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References

  1. Anderson J, Rosenfeld E (1990) Neurocomputing: foundations of research. MIT Press, Cambridge

    Google Scholar 

  2. Steinbuch K (1961) Die Lernmatrix. Kybernetik 1:36–45. https://doi.org/10.1007/BF00293853

    Article  MATH  Google Scholar 

  3. Willshaw D, Buneman O, Longuet-Higgins H (1969) Non-holographic associative memory. Nature 222:960–962. https://doi.org/10.1038/222960a0

    Article  Google Scholar 

  4. Anderson JA (1972) A simple neural network generating an interactive memory. Math Biosci 14:197–220. https://doi.org/10.1016/0025-5564(72)90075-2

    Article  MATH  Google Scholar 

  5. Kohonen T (1972) Correlation matrix memories. IEEE Trans Comput 4:353–359. https://doi.org/10.1109/TC.1972.5008975

    Article  MATH  Google Scholar 

  6. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Nat Acad Sci 79:2554–2558. https://doi.org/10.1073/pnas.79.8.2554

    Article  MathSciNet  MATH  Google Scholar 

  7. Abu-Mostafa Y, St. Jacques J (1985) Information capacity of the Hopfield model. IEEE Trans Inf Theory 31:461–464. https://doi.org/10.1109/TIT.1985.1057069

    Article  MATH  Google Scholar 

  8. Ritter GX, Sussner P, Diaz-de-Leon JL (1998) Morphological associative memories. IEEE Trans Neural Netw 9:281–293. https://doi.org/10.1109/72.661123

    Article  Google Scholar 

  9. Wu Y, Batalama SN (2000) An efficient learning algorithm for associative memories. IEEE Trans Neural Netw 11:1058–1066. https://doi.org/10.1109/72.870039

    Article  Google Scholar 

  10. Kimoto T, Okada M (2001) Mixed state on a sparsely encoded associative memory model. Biol Cybern 85:319–325. https://doi.org/10.1007/s004220100263

    Article  Google Scholar 

  11. Xu NS, Bai YF, Zhang L (2003) A novel high-order associative memory system via discrete Taylor series. IEEE Trans Neural Netw 14:734–747. https://doi.org/10.1109/TNN.2003.811700

    Article  Google Scholar 

  12. Tao Q, Liu X, Cui X (2005) A linear optimization neural network for associative memory. Appl Math Comput 171:1119–1128. https://doi.org/10.1016/j.amc.2005.01.123

    Article  MathSciNet  MATH  Google Scholar 

  13. Hélie S (2008) Energy minimization in the nonlinear dynamic recurrent associative memory. Neural Netw 21:1041–1044. https://doi.org/10.1016/j.neunet.2008.06.005

    Article  MATH  Google Scholar 

  14. Knoblauch A (2011) Neural associative memory with optimal bayesian learning. Neural Comput 23:1393–1451. https://doi.org/10.1162/NECO_a_00127

    Article  MathSciNet  MATH  Google Scholar 

  15. Wang M, Zhou T (2013) Multistability in a multidirectional associative memory neural network with delays. J Appl Math. https://doi.org/10.1155/2013/592056

    Article  MathSciNet  Google Scholar 

  16. Rendeiro D, Sacramento J, Wichert A (2014) Taxonomical associative memory. Cognit Comput 6:45–65. https://doi.org/10.1007/s12559-012-9198-4

    Article  Google Scholar 

  17. Heusel J, Löwe M, Vermet F (2015) On the capacity of an associative memory model based on neural cliques. Stat Probab Lett 106:256–261. https://doi.org/10.1016/j.spl.2015.07.026

    Article  MathSciNet  MATH  Google Scholar 

  18. Tarkov MS (2016) Oscillatory neural associative memories with synapses based on memristor bridges. Opt Mem Neural Netw 25:219–227. https://doi.org/10.3103/S1060992X16040068

    Article  Google Scholar 

  19. Wang SJ, Yang Z (2017) Effect of similarity between patterns in associative memory. Phys Rev E 95:012309. https://doi.org/10.1103/PhysRevE.95.012309

    Article  Google Scholar 

  20. Hassoun M (1993) Associative neural memories. Oxford University Press, New York

    MATH  Google Scholar 

  21. Cerón-Figueroa S, López-Yáñez I, Alhalabi W, Camacho-Nieto O, Villuendas-Rey Y, Aldape-Pérez M, Yáñez-Márquez C (2017) Instance-based ontology matching for e-learning material using an associative pattern classifier. Comput Hum Behav 69:218–225. https://doi.org/10.1016/j.chb.2016.12.039

    Article  Google Scholar 

  22. Moore J (1968) Elements of linear algebra and matrix theory. Mc Graw-Hill, New York

    Google Scholar 

  23. Rosen K (1995) Discrete mathematics and its applications. Mc Graw-Hill, New York

    Google Scholar 

  24. Yáñez-Márquez C, Sánchez-Fernández L, López-Yáñez I (2006) Alpha-Beta associative memories for gray level patterns. In: Wang J, Yi Z, Zurada J, Lu B, Yin H (eds) Advances in neural networks. Lecture notes in computer science, LNCS 3971. Springer, Heidelberg, pp 818–823. https://doi.org/10.1007/11759966_120

    Chapter  Google Scholar 

  25. Yáñez-Márquez C, Cruz-Meza M, Sánchez-Garfias F, López-Yáñez I (2007) Using Alpha-Beta associative memories to learn and recall RGB images. In: Liu D, Fei S, Hou Z, Zhang H, SunWang C (eds) Advances in neural networks. Lecture notes in computer science, LNCS 4493. Springer, Berlin, pp 828–833. https://doi.org/10.1007/978-3-540-72395-0_101

  26. Yáñez-Márquez C, Flores-Carapia R, López-Yáñez I (2007) An automatic color retrieval system based on artificial intelligence techniques. In: Proceedings of the 23rd ISPE international conference on CAD/CAM robotics and factories of the future, pp 345–349

  27. Aguilar-Torres M, Argüelles-Cruz A, Yáñez-Márquez C (2008) A real time artificial vision implementation of quality inspection of industrial products In: IEEE computer society, proceedings for the electronics, robotics, and automotive mechanics conference, pp 277–282. https://doi.org/10.1109/CERMA.2008.75

  28. Guzmán E, Pogrebnyak O, Sánchez-Fernández L, Yáñez-Márquez C (2008) A fast search algorithm for vector quantization based on associative memories. In: Ruiz-Shulcloper J, Kropatsch W (eds) Progress in pattern recognition, image analysis and applications. Lecture notes in computer science, LNCS 5197. Springer, Berlin, pp 487–495. https://doi.org/10.1007/978-3-540-85920-8_60

    Google Scholar 

  29. Guzmán E, Pogrebnyak O, Yáñez-Márquez C, Manrique P (2009) Vector quantization algorithm based on associative memories. In: Hernández A, Monroy R, Reyes C (eds) Advances in artificial intelligence. Lecture notes in computer science, LNCS 5845. Springer, Berlin, pp 324–336. https://doi.org/10.1007/978-3-642-05258-3_29

    Chapter  Google Scholar 

  30. Román-Godínez I, López-Yáñez I, Yáñez-Márquez C (2009) Classifying patterns in bioinformatics databases by using Alpha-Beta associative memories. In: Sidhu A, Dillon T (eds) Biomedical data and applications. Studies in computational intelligence, SCI 224. Springer, Berlin, pp 187210. https://doi.org/10.1007/978-3-642-02193-0_8

    Chapter  Google Scholar 

  31. López-Yáñez I, Flores-Carapia R, Yáñez-Márquez C, Camacho-Nieto O (2011) Automatic detection of cranial fractures in radiological images using a pattern classifier. Revista Facultad de Ingeniería 61:29–40

    Google Scholar 

  32. Aldape-Pérez M, Yáñez-Márquez C, Camacho-Nieto O, Argüelles-Cruz AJ (2012) A new tool for engineering education: hepatitis diagnosis using associative memories. Int J Eng Educ 28:1399–1405

    Google Scholar 

  33. Aldape-Pérez M, Yáñez-Márquez C, Camacho-Nieto O, Argüelles-Cruz AJ (2012) An associative memory approach to medical decision support systems. Comput Methods Progr Biomed 103:287–307. https://doi.org/10.1016/j.cmpb.2011.05.002

    Article  Google Scholar 

  34. Sepúlveda-Lima R, Yáñez-Márquez C, López-Yáñez I, Camacho-Nieto O (2012) A novel solution to the secure exchange of environmental engineering education data. Int J Eng Educ 28:1380–1387

    Google Scholar 

  35. Román-Godínez I, Yáñez-Márquez C (2007) Complete recall on Alpha-Beta heteroassociative memory. In: Gelbukh A, Kuri A (eds) Advances in artificial intelligence. Lecture notes in computer science, LNCS 4827, Springer, Berlin, pp 193–202. https://doi.org/10.1007/978-3-540-76631-5_19

  36. Aldape-Pérez M, Yáñez-Márquez C, Camacho-Nieto O (2008) Efficient pattern recalling using parallel Alpha-Beta associative memories. Res Comput Sci 35:147–156

    Google Scholar 

  37. Catalán-Salgado E, Yáñez-Márquez C, Argüelles-Cruz A (2008) Simplification of the learning phase in the Alpha-Beta associative memories. In: IEEE computer society, proceedings. Electronics, robotics, and automotive mechanics conference, pp 428–433. https://doi.org/10.1109/CERMA.2008.72

  38. Catalán-Salgado E, Yáñez-Márquez C, Figueroa-Nazuno J (2012) Significative learning using Alpha-Beta associative memories. In: Alvarez L, Mejail M, Gomez L, Jacobo J (eds) Progress in pattern recognition, image analysis, computer vision, and applications. Lecture notes in computer science, LNCS 7441, Springer, Berlin, pp 535–542. https://doi.org/10.1007/978-3-642-33275-3_66

    Chapter  Google Scholar 

  39. López-Yáñez I, Yáñez-Márquez C (2006) Using binary decision diagrams to efficiently represent Alpha-Beta associative memories. In: IEEE computer society, proceedings of the electronics, robotics, and automotive mechanics conference, pp 172–177. https://doi.org/10.1109/CERMA.2006.96

  40. Yáñez-Márquez C, López-Yáñez I, Camacho-Nieto O, Argüelles-Cruz AJ (2013) BDD-based algorithm for the minimum spanning tree in wireless ad hoc network routing. IEEE Latin Am Trans 11:600–601. https://doi.org/10.1109/TLA.2013.6502868

    Article  Google Scholar 

  41. Luna-Benoso B, Yáñez-Márquez C (2012) Alpha-Beta cellular automata. Computación y Sistemas 16:471–479. https://doi.org/10.13053/cys-16-4-1438

    Article  Google Scholar 

  42. Luna-Benoso B, Flores-Carapia R, Yáñez-Márquez C (2013) Associative memories based on cellular automata: an application to pattern recognition. Appl Math Sci 7:857–866. https://doi.org/10.12988/ams.2013.13077

    Article  MathSciNet  Google Scholar 

  43. Aldape-Pérez M, Yáñez-Márquez C, Argüelles-Cruz A (2008) FPGA implementation of parallel Alpha-Beta associative memories. In: Campilho A, Kamel M (eds) Image analysis and recognition. Lecture notes in computer science, LNCS 5112. Springer, Berlin, pp 1081–1090. https://doi.org/10.1007/978-3-540-69812-8_108

  44. López-Yáñez I, Argüelles-Cruz AJ, Camacho-Nieto O, Yáñez-Márquez C (2011) Pollutants time series prediction using the Gamma classifier. Int J Comput Intell Syst 4:680–711. https://doi.org/10.2991/ijcis.2011.4.4.23

    Article  Google Scholar 

  45. Yáñez-Márquez C, López-Yáñez I, Sáenz-Morales G (2008) Analysis and prediction of air quality data with the Gamma classifier. In: Ruiz-Shulcloper J, Kropatsch W (eds.) Progress in pattern recognition, image analysis and applications. Lecture notes in computer science, LNCS 5197. Springer, Berlin, pp 651–658. https://doi.org/10.1007/978-3-540-85920-8_79

    Google Scholar 

  46. López-Yáñez I, Yáñez-Márquez C, Sáenz-Morales G (2008) Application of the Gamma classifier to environmental data prediction. In: IEEE computer society, proceedings of the electronics, robotics, and automotive mechanics conference, pp 80–84. https://doi.org/10.1109/CERMA.2008.35

  47. López-Yáñez I, Yáñez-Márquez C, Silva García V (2009) Forecasting air quality data with the Gamma classifier. In: Yin P (ed) Pattern recognition. In-Tech, Croacia, pp 499512. https://doi.org/10.5772/7528

    Google Scholar 

  48. Argüelles-Cruz A, Yáñez-Márquez C, López-Yáñez I, Camacho-Nieto O (2011) Prediction of CO and NOx levels in Mexico City using associative models. In: Iliadis L et al. (eds) Artificial intelligence applications and innovations. Advances in information and communication technology AICT 364. Springer, Heidelberg, pp. 13322. https://doi.org/10.1007/978-3-642-23960-1

    Google Scholar 

  49. López-Martín C, López-Yáñez I, Yáñez-Márquez C (2012) Application of Gamma classifier to development effort prediction of software projects. Appl Math Inf Sci 6:411–418

    Google Scholar 

  50. López-Yáñez I, Sheremetov L, Yáñez-Márquez C (2013) Associative model for the forecasting of time series based on the Gamma classifier. In: Carrasco-Ochoa J, Martínez-Trinidad J, Salas J, Sanniti G (eds) Pattern recognition. Lecture notes in computer science, LNCS 7914. Springer, Berlin, pp 304–313. https://doi.org/10.1007/978-3-642-38989-4_31

    Google Scholar 

  51. López-Yáñez I, Sheremetov L, Yáñez-Márquez C (2014) A novel associative model for time series data mining. Pattern Recogn Lett 41:23–33. https://doi.org/10.1016/j.patrec.2013.11.008

    Article  Google Scholar 

  52. Jurado-Sánchez OS, Yáñez-Márquez C, Camacho-Nieto O, López-Yáñez I (2014) Currency exchange rate forecasting using associative models. Res Comput Sci 78:67–76

    Google Scholar 

  53. Ramírez-Ramírez A, López-Yáñez I, Villuendas-Rey Y, Yáñez-Márquez C (2015) Evolutive improvement of parameters in an associative classifier. IEEE Latin Am Trans 13:1550–1555. https://doi.org/10.1109/TLA.2015.7112014

    Article  Google Scholar 

  54. Ramírez Ramírez A, López-Yáñez I, Villuendas-Rey Y, Yáñez-Márquez C (2015) Improving parameters of the Gamma associative classifier using differential evolution. Res Comput Sci 98:59–72

    Google Scholar 

  55. Uriarte-Arcia AV, Yáñez-Márquez C, Gama J, López-Yáñez I, Camacho-Nieto O (2015) Data stream classification based on the Gamma classifier. Math Probl Eng. https://doi.org/10.1155/2015/939175

    Article  Google Scholar 

  56. Román-Godínez I, López-Yáñez I, Yáñez-Márquez C (2006) A new classifier based on associative memories. In: Proceedings of the 15th international conference on computing, pp 55–59. https://doi.org/10.1109/CIC.2006.13

  57. Román-Godínez I, Garibay-Orijel C, Yáñez-Márquez C (2011) Identification of functional sequences using associative memories. Revista Mexicana de Ingeniería Biomédica 32:109–118

    Google Scholar 

  58. López-Leyva L, Yáñez-Márquez C, López-Yáñez I (2007) A new efficient model of support vector machines: Alpha-Beta SVM. In: Proceedings of the 23rd. ISPE international conference on CAD/CAM robotics and factories of the future, pp 300–310

  59. López-Leyva L, Yáñez-Márquez C, Flores-Carapia R, Camacho-Nieto O (2008) Handwritten digit classification based on Alpha-Beta associative model. In: Ruiz-Shulcloper J, Kropatsch W (eds) Progress in pattern recognition, image analysis and applications. Lecture notes in computer science, LNCS 5197. Springer, Berlin, pp 437–444. https://doi.org/10.1007/978-3-540-85920-8_54

    Google Scholar 

  60. Solís-Villarreal JF, Yáñez-Márquez C, Suárez-Guerra S (2011) Automatic emotional speech recognition with Alpha-Beta SVM associative memories. Polibits 44:19–23

    Article  Google Scholar 

  61. Acevedo-Mosqueda ME, Yáñez-Márquez C, López-Yáñez I (2007) Alpha-Beta bidirectional associative memories: theory and applications. Neural Process Lett 26:1–40. https://doi.org/10.1007/s11063-007-9040-2

    Article  Google Scholar 

  62. Acevedo-Mosqueda ME, Yáñez-Márquez C, López-Yáñez I (2006) Alpha-Beta bidirectional associative memories based translator. Int J Comput Sci Netw Secur 6:190–194

    Google Scholar 

  63. Acevedo-Mosqueda ME, Yáñez-Márquez C, López-Yáñez I (2006) Complexity of Alpha-Beta bidirectional associative memories. In: Gelbukh A, Reyes-Garcia C (eds) Advances in artificial intelligence. Lecture notes in computer science, LNCS 4293. Springer, Berlin, pp 357–366. https://doi.org/10.1007/11925231_34

    Google Scholar 

  64. Acevedo ME, Yáñez-Márquez C, Acevedo MA (2013) Bidirectional associative memories: different approaches. ACM Comput Surv 45:18:1–18:30. https://doi.org/10.1145/2431211.2431217

    Article  Google Scholar 

  65. Acevedo ME, Yáñez-Márquez C, Acevedo MA (2010) Associative models for storing and retrieving concept lattices. Math Probl Eng. https://doi.org/10.1155/2010/356029

    Article  MathSciNet  MATH  Google Scholar 

  66. Aldape-Pérez M, Yáñez-Márquez C, López -Leyva LO (2006) Optimized implementation of a pattern classifier using feature set reduction. Res Comput Sci 24:11–20

    Google Scholar 

  67. Aldape-Pérez M, Yáñez-Márquez C, López -Leyva L (2006) Feature selection using a hybrid associative classifier with masking techniques. In: Proceedings of the fifth Mexican international conference on artificial intelligence, pp 151–160. https://doi.org/10.1109/MICAI.2006.15

  68. Aldape-Pérez M, Yáñez-Márquez C, Argüelles-Cruz A (2007) Optimized associative memories for feature selection. In: MartıJ, Benedí J, Mendonça A, Serrat J (eds) Pattern recognition and image analysis. Lecture notes in computer science, LNCS 4477. Springer, Berlin, pp 435–442. https://doi.org/10.1007/978-3-540-72847-4_56

  69. Aldape-Pérez M, Yáñez-Márquez C, Camacho-Nieto O, Ferreira-Santiago A (2013) Feature selection using associative memory paradigm and parallel computing. Computación y Sistemas 17:41–52

    Google Scholar 

  70. Ferreira Santiago A, Yáñez-Márquez C, Aldape Perez M, López-Yáñez I (2014) Evolutionary approach to feature selection with associative models. Res Comput Sci 78:111–122

    Google Scholar 

  71. Cleofas-Sánchez L, Camacho-Nieto O, Sánchez-Garreta JS, Yáñez-Márquez C, Valdovinos-Rosas RM (2014) Equilibrating the recognition of the minority class in the imbalance context. Appl Math Inf Sci 8:27–36. https://doi.org/10.12785/amis/080103

    Article  Google Scholar 

  72. López-Yáñez I, Yáñez-Márquez C, Camacho-Nieto O, Aldape-Pérez M, Argüelles-Cruz AJ (2015) Collaborative learning in postgraduate level courses. Comput Hum Behav 51B:938–944. https://doi.org/10.1016/j.chb.2014.11.055

    Article  Google Scholar 

  73. Aldape-Pérez M, Yáñez-Márquez C, Camacho-Nieto O, López-Yáñez I, Argüelles-Cruz AJ (2015) Collaborative learning based on associative models: application to pattern classification in medical datasets. Comput Hum Behav 51B:771–779. https://doi.org/10.1016/j.chb.2014.11.091

    Article  Google Scholar 

  74. Cerón-Figueroa S, López-Yáñez I, Villuendas-Rey Y, Camacho-Nieto O, Aldape-Pérez M, Yáñez-Márquez C (2017) Instance-based ontology matching for open and distance learning materials. Int Rev Res Open Distrib Learn 18:177–195. https://doi.org/10.19173/irrodl.v18i1.2681

    Article  Google Scholar 

  75. García-Floriano A, Ferreira-Santiago A, Yáñez-Márquez C, Camacho-Nieto O, Aldape-Pérez M, Villuendas-Rey Y (2017) Social web content enhancement in a distance learning environment: intelligent metadata generation for resources. Int Rev Res Open Distrib Learn 18:161–176. https://doi.org/10.19173/irrodl.v18i1.2646

    Article  Google Scholar 

  76. Ferreira-Santiago A, López-Martín C, Yáñez-Márquez C (2016) Metaheuristic optimization of multivariate adaptive regression splines for predicting the schedule of software projects. Neural Comput Appl 27:22292240. https://doi.org/10.1007/s00521-015-2003-z

    Article  Google Scholar 

  77. Uriarte-Arcia AV, López-Yáñez I, Yáñez-Márquez C (2014) One-hot vector hybrid associative classifier for medical data classification. PLoS ONE 9:e95715. https://doi.org/10.1371/journal.pone.0095715

    Article  Google Scholar 

  78. García-Floriano A, Camacho-Nieto O, Yáñez-Márquez C (2015) Heaviside’s classifier. NovaScientia 7:365–397. https://doi.org/10.21640/ns.v7i14.269

    Article  Google Scholar 

  79. Ortiz-Ángeles S, Villuendas-Rey Y, López-Yáñez I, Camacho-Nieto O, Yáñez-Márquez C (2017) Electoral preferences prediction of the YouGov social network users based on computational intelligence algorithms. J Univers Comput Sci 23:304–326

    MathSciNet  Google Scholar 

  80. Ramírez-Rubio R, Aldape-Pérez M, Yáñez-Márquez C, López-Yáñez I, Camacho-Nieto O (2017) Pattern classification using smallest normalized difference associative memory. Pattern Recogn Lett 93:104–112. https://doi.org/10.1016/j.patrec.2017.02.013

    Article  Google Scholar 

  81. Villuendas-Rey Y, Rey-Benguría C, Ferreira-Santiago A, Camacho-Nieto O, Yáñez-Márquez C (2017) The naïve associative classifier (NAC): a novel, simple, transparent, and accurate classification model evaluated on financial data. Neurocomputing. https://doi.org/10.1016/j.neucom.2017.03.085

    Article  Google Scholar 

  82. Antón-Vargas JA, Villuendas-Rey Y, Yáñez-Márquez C, López-Yáñez I, Camacho-Nieto O (2017) Improving the performance of an associative classifier by Gamma rough sets based instance selection. Int J Pattern Recogn Artif Intell. https://doi.org/10.1142/S0218001418600091

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the Instituto Politécnico Nacional (Secretaría Académica, COFAA, SIP, CIDETEC, and CIC), the CONACyT, and SNI for their support to develop this work.

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Yáñez-Márquez, C., López-Yáñez, I., Aldape-Pérez, M. et al. Theoretical Foundations for the Alpha-Beta Associative Memories: 10 Years of Derived Extensions, Models, and Applications. Neural Process Lett 48, 811–847 (2018). https://doi.org/10.1007/s11063-017-9768-2

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