Skip to main content
Springer Nature Link
Log in

A Survey on Computational Intelligence Techniques in Learning and Memory

  • Conference paper
  • First Online:
Computational Intelligence in Communications and Business Analytics (CICBA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1579))

  • 526 Accesses

Abstract

Learning and Memory is a branch of artificial intelligence that studies the critical brain functions in order to create novel computational intelligence techniques and methods focused on learning and memory. As a consequence, human intelligence demands a review on computational intelligence techniques in learning and memory. This paper explains why without learning, the goal of achieving human intelligence is still a long bit away. This paper discusses hippocampus learning, human learning and memory, hidden markov model, behavioral plasticity in learning and memory, PET and fMRI, as well as the neurological illnesses. Here, we outline the important work done in the domain of learning as well as memory. This paper examines the evolution of learning and memory during several decades. This study also discusses the merits and limitations of several learning and memory models and techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Botkin, J.W., Elmandjra, M., Malitza, M.: No Limits to Learning: Bridging the Human Gap: The Report to the Club of Rome. Elsevier (2014)

    Google Scholar 

  2. Goldstone, R.L.: Perceptual learning. Annu. Rev. Psychol. 49(1), 585–612 (1998)

    Article  Google Scholar 

  3. Schwabe, L., et al.: Stress modulates the use of spatial versus stimulus-response learning strategies in humans. Learn. Mem. 14(1–2), 109–116 (2007)

    Article  Google Scholar 

  4. Singer, R.N.: Motor Learning and Human Performance: An Application to Motor Skills and Movement Behaviors. Macmillan, New York (1980)

    Google Scholar 

  5. Koller, D., et al.: Introduction to Statistical Relational Learning. MIT Press (2007)

    Google Scholar 

  6. Tolman, E.C., Ritchie, B.F., Kalish, D.: Studies in spatial learning. II. Place learning versus response learning. J. Exp. Psychol. 36(3), 221 (1946)

    Article  Google Scholar 

  7. Tyre, M.J., Orlikowski, W.J.: The episodic process of learning by using. Int. J. Technol. Manag. 11(7–8), 790–798 (1996)

    Google Scholar 

  8. Bandura, A.: Observational learning. In: The International Encyclopedia of Communication (2008)

    Google Scholar 

  9. Weisz, V.I., Argibay, P.F.: A putative role for neurogenesis in neurocomputational terms: inferences from a hippocampal model. Cognition 112(2), 229–240 (2009)

    Article  Google Scholar 

  10. Chung, P.C., Liu, C.D.: A daily behavior enabled hidden Markov model for human behavior understanding. Pattern Recogn. 41(5), 1572–1580 (2008)

    Article  MATH  Google Scholar 

  11. Antonucci, A., De Rosa, R., Giusti, A.: Action recognition by imprecise hidden Markov models. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), p. 1. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) (2011)

    Google Scholar 

  12. Zhang, B.T.: Hypernetworks: a molecular evolutionary architecture for cognitive learning and memory. IEEE Comput. Intell. Mag. 3(3) (2008)

    Google Scholar 

  13. Zhang, B.T.: Cognitive learning and the multimodal memory game: toward human-level machine learning. In: IEEE International Joint Conference on Neural Networks, IJCNN 2008. IEEE World Congress on Computational Intelligence, pp. 3261–3267, June 2008

    Google Scholar 

  14. Hajimirsadeghi, H., Ahmadabadi, M.N., Araabi, B.N.: Conceptual imitation learning based on perceptual and functional characteristics of action. IEEE Trans. Auton. Ment. Dev. 5(4), 311–325 (2013)

    Article  Google Scholar 

  15. Kemere, C., et al.: Detecting neural-state transitions using hidden Markov models for motor cortical prostheses. J. Neurophysiol. 100(4), 2441–2452 (2008)

    Article  Google Scholar 

  16. Kaczmarek, L.: Gene expression in learning processes. Acta Neurobiol. Exp. 60(3), 419–424 (2000)

    Google Scholar 

  17. Alberini, C.M.: Transcription factors in long-term memory and synaptic plasticity. Physiol. Rev. 89(1), 121–145 (2009)

    Article  Google Scholar 

  18. Sato, N., Yamaguchi, Y.: Simulation of human episodic memory by using a computational model of the hippocampus. In: Advances in Artificial Intelligence 2010 (2010)

    Google Scholar 

  19. Widloski, J., Fiete, I.: How does the brain solve the computational problems of spatial navigation? In: Derdikman, D., Knierim, J.J. (eds.) Space, Time and Memory in the Hippocampal Formation, pp. 373–407. Springer, Vienna (2014). https://doi.org/10.1007/978-3-7091-1292-2_14

    Chapter  Google Scholar 

  20. Atallah, H.E., Frank, M.J., O’Reilly, R.C.: Hippocampus, cortex, and basal ganglia: insights from computational models of complementary learning systems. Neurobiol. Learn. Mem. 82(3), 253–267 (2004)

    Article  Google Scholar 

  21. Florian, B., Sepp, K., Joshua, H., Richard, H.: Hidden Markov models in the neurosciences. In: Hidden Markov Models, Theory and Applications (2011)

    Google Scholar 

  22. Lee, Y.S.: Genes and signaling pathways involved in memory enhancement in mutant mice. Mol. Brain 7(1), 43 (2014)

    Article  Google Scholar 

  23. Tran, T., Bui, H., Venkatesh, S.: Human activity learning and segmentation using partially hidden discriminative models. arXiv preprint arXiv:1408.3081 (2014)

  24. Park, C., Ahn, J., Kim, H., Park, S.: Integrative gene network construction to analyze cancer recurrence using semi-supervised learning. PLoS ONE 9(1), e86309 (2014)

    Article  Google Scholar 

  25. Chalmers, D.J.: The evolution of learning: an experiment in genetic connectionism. In: Connectionist Models, pp. 81–90 (1991)

    Google Scholar 

  26. Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350(6266), 1332–1338 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  27. Phuong, T.M., Nhung, N.P.: Predicting gene function using similarity learning. BMC Genomics 14(4), S4 (2013)

    Article  Google Scholar 

  28. Kello, C.T., Rodny, J., Warlaumont, A.S., Noelle, D.C.: Plasticity, learning, and complexity in spiking networks. Crit. Rev.™ Biomed. Eng. 40(6) (2012)

    Google Scholar 

  29. Frank, M.J., Fossella, J.A.: Neurogenetics and pharmacology of learning, motivation, and cognition. Neuropsychopharmacology 36(1), 133 (2011)

    Article  Google Scholar 

  30. Cameron, H.A., Glover, L.R.: Adult neurogenesis: beyond learning and memory. Annu. Rev. Psychol. 66, 53–81 (2015)

    Article  Google Scholar 

  31. Conrad, C.D.: A critical review of chronic stress effects on spatial learning and memory. Prog. Neuropsychopharmacol. Biol. Psychiatry 34(5), 742–755 (2010)

    Article  Google Scholar 

  32. Friedman, D., Johnson Jr, R.: Event-related potential (ERP) studies of memory encoding and retrieval: a selective review. Microsc. Res. Tech. 51(1), 6–28 (2000)

    Article  Google Scholar 

  33. Herz, R.S., Engen, T.: Odor memory: review and analysis. Psychon. Bull. Rev. 3(3), 300–313 (1996)

    Article  Google Scholar 

  34. Kantak, S.S., Winstein, C.J.: Learning–performance distinction and memory processes for motor skills: a focused review and perspective. Behav. Brain Res. 228(1), 219–231 (2012)

    Article  Google Scholar 

  35. Moreira, P.S., Almeida, P.R., Leite-Almeida, H., Sousa, N., Costa, P.: Impact of chronic stress protocols in learning and memory in rodents: systematic review and meta-analysis. PLoS ONE 11(9), e0163245 (2016)

    Article  Google Scholar 

  36. Peng, S., Zhang, Y., Zhang, J., Wang, H., Ren, B.: ERK in learning and memory: a review of recent research. Int. J. Mol. Sci. 1, 222–232 (2010)

    Article  Google Scholar 

  37. Reber, P.J.: The neural basis of implicit learning and memory: a review of neuropsychological and neuroimaging research. Neuropsychologia 51(10), 2026–2042 (2013)

    Article  Google Scholar 

  38. Soderstrom, N.C., Bjork, R.A.: Learning versus performance: an integrative review. Perspect. Psychol. Sci. 10(2), 176–199 (2015)

    Article  Google Scholar 

  39. Curran, H.V.: Benzodiazepines, memory and mood: a review. Psychopharmacology 105(1), 1–8 (1991)

    Article  Google Scholar 

  40. Zhao, W.Q., Chen, H., Quon, M.J., Alkon, D.L.: Insulin and the insulin receptor in experimental models of learning and memory. Eur. J. Pharmacol. 490(1–3), 71–81 (2004)

    Article  Google Scholar 

  41. Berka, C., et al.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78(5), B231–B244 (2007)

    Google Scholar 

  42. Glisky, E.L., Schacter, D.L., Tulving, E.: Computer learning by memory-impaired patients: acquisition and retention of complex knowledge. Neuropsychologia 24(3), 313–328 (1986)

    Article  Google Scholar 

  43. McClelland, J.L., McNaughton, B.L., O’reilly, R.C.: Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol. Rev. 102(3), 419 (1995)

    Article  Google Scholar 

  44. Sutherland, R.J., Whishaw, I.Q., Kolb, B.: Contributions of cingulate cortex to two forms of spatial learning and memory. J. Neurosci. 8(6), 1863–1872 (1988)

    Article  Google Scholar 

  45. Barto, A.G., Sutton, R.S., Brouwer, P.S.: A reinforcement learning associative memory. Biol. Cybern 40(20), 2 (1981)

    MATH  Google Scholar 

  46. Barnes, C.A.: Spatial learning and memory processes: the search for their neurobiological mechanisms in the rat. Trends Neurosci. 11(4), 163–169 (1988)

    Article  Google Scholar 

  47. Berchtold, N.C., Castello, N., Cotman, C.W.: Exercise and time-dependent benefits to learning and memory. Neuroscience 167(3), 588–597 (2010)

    Article  Google Scholar 

  48. Blokland, A.: Acetylcholine: a neurotransmitter for learning and memory? Brain Res. Rev. 21(3), 285–300 (1995)

    Article  Google Scholar 

  49. Paivio, A.: Mental imagery in associative learning and memory. Psychol. Rev. 76(3), 241 (1969)

    Article  Google Scholar 

  50. Alba, J.W., Hasher, L.: Is memory schematic? Psychol. Bull. 93(2), 203 (1983)

    Article  Google Scholar 

  51. Cao, L., et al.: VEGF links hippocampal activity with neurogenesis, learning and memory. Nat. Genet. 36(8), 827–835 (2004)

    Article  Google Scholar 

  52. Chun, M.M., Jiang, Y.: Contextual cueing: implicit learning and memory of visual context guides spatial attention. Cogn. Psychol. 36(1), 28–71 (1998)

    Article  MathSciNet  Google Scholar 

  53. Passolunghi, M.C., Vercelloni, B., Schadee, H.: The precursors of mathematics learning: working memory, phonological ability and numerical competence. Cogn. Dev. 22(2), 165–184 (2007)

    Article  Google Scholar 

  54. Squire, L.R.: Declarative and nondeclarative memory: multiple brain systems supporting learning and memory. J. Cogn. Neurosci. 4(3), 232–243 (1992)

    Article  Google Scholar 

  55. Deng, W., Aimone, J.B., Gage, F.H.: New neurons and new memories: how does adult hippocampal neurogenesis affect learning and memory? Nat. Rev. Neurosci. 11(5), 339–350 (2010)

    Article  Google Scholar 

  56. Desmond, J.E., Fiez, J.A.: Neuroimaging studies of the cerebellum: language, learning and memory. Trends Cogn. Sci. 2(9), 355–362 (1998)

    Article  Google Scholar 

  57. Du, H., et al.: Cyclophilin D deficiency attenuates mitochondrial and neuronal perturbation and ameliorates learning and memory in Alzheimer’s disease. Nat. Med. 10, 1097–1105 (2008)

    Article  Google Scholar 

  58. Dubnau, J., Tully, T.: Gene discovery in Drosophila: new insights for learning and memory. Annu. Rev. Neurosci. 21(1), 407–444 (1998)

    Article  Google Scholar 

  59. Eysenck, M.W.: Anxiety, learning, and memory: a reconceptualization. J. Res. Pers. 13(4), 363–385 (1979)

    Article  Google Scholar 

  60. Morris, G.P., Clark, I.A., Zinn, R., Vissel, B.: Microglia: a new frontier for synaptic plasticity, learning and memory, and neurodegenerative disease research. Neurobiol. Learn. Mem. 105, 40–53 (2013)

    Article  Google Scholar 

  61. Gold, P.E.: Acetylcholine modulation of neural systems involved in learning and memory. Neurobiol. Learn. Mem. 80(3), 194–210 (2003)

    Article  Google Scholar 

  62. Payan, A., Montana, G.: Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. arXiv preprint arXiv:1502.02506 (2015)

  63. Blennow, K., Dubois, B., Fagan, A.M., Lewczuk, P., de Leon, M.J., Hampel, H.: Clinical utility of cerebrospinal fluid biomarkers in the diagnosis of early Alzheimer’s disease. Alzheimer’s Dement. 11(1), 58–69 (2015)

    Article  Google Scholar 

  64. Escott-Price, V., Shoai, M., Pither, R., Williams, J., Hardy, J.: Polygenic score prediction captures nearly all common genetic risk for Alzheimer’s disease. Neurobiol. Aging 49, 214-e7 (2017)

    Article  Google Scholar 

  65. Eskildsen, S.F., Coupé, P., Fonov, V.S., Pruessner, J.C., Collins, D.L., Alzheimer’s Disease Neuroimaging Initiative: Structural imaging biomarkers of Alzheimer’s disease: predicting disease progression. Neurobiol. Aging 36, S23-31 (2015)

    Article  Google Scholar 

  66. Levy, B.R., Ferrucci, L., Zonderman, A.B., Slade, M.D., Troncoso, J., Resnick, S.M.: A culture–brain link: negative age stereotypes predict Alzheimer’s disease biomarkers. Psychol. Aging 31(1), 82 (2016)

    Article  Google Scholar 

  67. Jack, C.R., Jr., et al.: NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimer’s Dement. 14(4), 535–562 (2018)

    Article  Google Scholar 

  68. Kauppi, K., et al.: Combining polygenic hazard score with volumetric MRI and cognitive measures improves prediction of progression from mild cognitive impairment to Alzheimer’s disease. Front. Neurosci. 12, 260 (2018)

    Article  Google Scholar 

  69. Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 935–942 (2009)

    Google Scholar 

  70. Xia, C., Fu, L., Liu, Z., Liu, H., Chen, L., Liu, Y.: Aquatic toxic analysis by monitoring fish behavior using computer vision: a recent progress. J. Toxicol. (2018)

    Google Scholar 

  71. Sathyanarayana, A., Boyraz, P., Purohit, Z., Lubag, R., Hansen, J.H.: Driver adaptive and context aware active safety systems using CAN-bus signals. In: 2010 IEEE Intelligent Vehicles Symposium, pp. 1236–1241 (2010)

    Google Scholar 

  72. Atallah, L., Yang, G.Z.: The use of pervasive sensing for behaviour profiling—A survey. Pervasive Mob. Comput. 5(5), 447–464 (2009)

    Article  Google Scholar 

  73. Bicego, M., Grosso, E., Tistarelli, M.: Person authentication from video of faces: a behavioral and physiological approach using pseudo hierarchical hidden Markov models. In: Zhang, D., Jain, A.K. (eds.) ICB 2006. LNCS, vol. 3832, pp. 113–120. Springer, Heidelberg (2005). https://doi.org/10.1007/11608288_16

    Chapter  Google Scholar 

  74. Ko, T.: A survey on behavior analysis in video surveillance for homeland security applications. In: 2008 37th IEEE Applied Imagery Pattern Recognition Workshop, pp. 1–8. IEEE (2008)

    Google Scholar 

  75. Cheng, S.Y., Park, S., Trivedi, M.M.: Multi-spectral and multi-perspective video arrays for driver body tracking and activity analysis. Comput. Vis. Image Underst. 106(2–3), 245–257 (2007)

    Article  Google Scholar 

  76. Syeda-Mahmood, T., Ponceleon, D.: Learning video browsing behavior and its application in the generation of video previews. In: Proceedings of the Ninth ACM International Conference on Multimedia, pp. 119–128 (2009)

    Google Scholar 

  77. Blasch, E., et al.: Video-based activity analysis using the L1 tracker on VIRAT data. In: 2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1–8. IEEE (2013)

    Google Scholar 

  78. Hautzel, H., Mottaghy, F.M., Specht, K., Müller, H.W., Krause, B.J.: Evidence of a modality-dependent role of the cerebellum in working memory? An fMRI study comparing verbal and abstract n-back tasks. Neuroimage 47(4), 2073–2082 (2009)

    Article  Google Scholar 

  79. Haslinger, B., et al.: The role of lateral premotor–cerebellar–parietal circuits in motor sequence control: a parametric fMRI study. Cogn. Brain Res. 13(2), 159–168 (2002)

    Article  Google Scholar 

  80. Phan, K.L., Wager, T., Taylor, S.F., Liberzon, I.: Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and fMRI. Neuroimage 16(2), 331–348 (2002)

    Article  Google Scholar 

  81. Carroll, P.A., Freie, B.W., Mathsyaraja, H., Eisenman, R.N.: The MYC transcription factor network: balancing metabolism, proliferation and oncogenesis. Front. Med. 12(4), 412–425 (2018)

    Article  Google Scholar 

  82. Morgunova, E., et al.: Two distinct DNA sequences recognized by transcription factors represent enthalpy and entropy optima. Elife 7, e32963 (2018)

    Article  Google Scholar 

  83. Levine, M., Tjian, R.: Transcription regulation and animal diversity. Nature 424(6945), 147 (2003)

    Article  Google Scholar 

  84. Tanimizu, T., Kono, K., Kida, S.: Brain networks activated to form object recognition memory. Brain Res. Bull. 141, 27–34 (2018)

    Article  Google Scholar 

  85. Kacsoh, B.Z., et al.: New Drosophila long-term memory genes revealed by assessing computational function prediction methods. G3: Genes Genomes Genet. 9(1), 251–267 (2019)

    Google Scholar 

  86. Meng, L., et al.: Proteomics reveals the molecular underpinnings of stronger learning and memory in eastern compared to western bees. Mol. Cell. Proteomics 17(2), 255–269 (2018)

    Article  Google Scholar 

  87. Winbush, A., Reed, D., Chang, P.L., Nuzhdin, S.V., Lyons, L.C., Arbeitman, M.N.: Identification of gene expression changes associated with long-term memory of courtship rejection in Drosophila males. G3: Genes Genomes Genet. 2(11), 1437–1445 (2012)

    Article  Google Scholar 

  88. Maquet, P.: The role of sleep in learning and memory. Science 294(5544), 1048–1052 (2001)

    Article  Google Scholar 

  89. Kim, Y.C., Lee, H.G., Han, K.A.: D1 dopamine receptor dDA1 is required in the mushroom body neurons for aversive and appetitive learning in Drosophila. J. Neurosci. 27(29), 7640–7647 (2007)

    Article  Google Scholar 

  90. Dulac, C.: Brain function and chromatin plasticity. Nature 465(7299), 728 (2010)

    Article  Google Scholar 

  91. Hobert, O.: Behavioral plasticity in C. elegans: paradigms, circuits, genes. J. Neurobiol. 54(1), 203–223 (2003)

    Article  Google Scholar 

  92. Hyman, S.E., Malenka, R.C., Nestler, E.J.: Neural mechanisms of addiction: the role of reward-related learning and memory. Annu. Rev. Neurosci. 29, 565–598 (2006)

    Article  Google Scholar 

  93. Rittschof, C.C., Hughes, K.A.: Advancing behavioural genomics by considering timescale. Nat. Commun. 9(1), 489 (2018)

    Article  Google Scholar 

  94. Whitfield, C.W., Cziko, A.M., Robinson, G.E.: Gene expression profiles in the brain predict behavior in individual honey bees. Science 302(5643), 296–299 (2003)

    Article  Google Scholar 

  95. Jiang, F., et al.: Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol. 2(4), 230–243 (2017)

    Article  Google Scholar 

  96. Si, B., Song, E.: Recent advances in the detection of neurotransmitters. Chemosensors 6(1), 1 (2018)

    Article  Google Scholar 

  97. Fernandez-Lozano, C., Cuinas, R.F., Seoane, J.A., Fernandez-Blanco, E., Dorado, J., Munteanu, C.R.: Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models. J. Theor. Biol. 384, 50–58 (2015)

    Article  MATH  Google Scholar 

  98. Pan, J.X., et al.: Diagnosis of major depressive disorder based on changes in multiple plasma neurotransmitters: a targeted metabolomics study. Transl. Psychiatry 8(1), 1 (2018)

    Article  Google Scholar 

  99. Moon, J.M., Thapliyal, N., Hussain, K.K., Goyal, R.N., Shim, Y.B.: Conducting polymer-based electrochemical biosensors for neurotransmitters: a review. Biosens. Bioelectron. 102, 540–552 (2018)

    Article  Google Scholar 

  100. Tavakolian-Ardakani, Z., Hosu, O., Cristea, C., Mazloum-Ardakani, M., Marrazza, G.: Latest trends in electrochemical sensors for neurotransmitters: a review. Sensors 19(9), 2037 (2019)

    Article  Google Scholar 

  101. Pocock, J.M., Kettenmann, H.: Neurotransmitter receptors on microglia. Trends Neurosci. 30(10), 527–535 (2007)

    Article  Google Scholar 

  102. Lau, C.H., King, G.F., Mobli, M.: Molecular basis of the interaction between gating modifier spider toxins and the voltage sensor of voltage-gated ion channels. Sci. Rep. 6, 34333 (2016)

    Article  Google Scholar 

  103. Streit, J., Kleinlogel, S.: Dynamic all-optical drug screening on cardiac voltage-gated ion channels. Sci. Rep. 8(1), 1153 (2018)

    Article  Google Scholar 

  104. Zhang, H., Reichert, E., Cohen, A.E.: Optical electrophysiology for probing function and pharmacology of voltage-gated ion channels. Elife 5, e15202 (2016)

    Article  Google Scholar 

  105. Zamponi, G.W., Han, C., Waxman, S.G.: Voltage-gated ion channels as molecular targets for pain. In: Tuszynski, M.H. (ed.) Translational Neuroscience, pp. 415–436. Springer, Boston, MA (2016). https://doi.org/10.1007/978-1-4899-7654-3_22

    Chapter  Google Scholar 

  106. Lehmann-Horn, F., Jurkat-Rott, K.: Voltage-gated ion channels and hereditary disease. Physiol. Rev. 79(4), 1317–1372 (1999)

    Article  Google Scholar 

  107. Yellen, G.: The moving parts of voltage-gated ion channels. Q. Rev. Biophys. 31(3), 239–295 (1998)

    Article  Google Scholar 

  108. Roscow, E.L., Chua, R., Costa, R.P., Jones, M.W., Lepora, N.: Learning offline: memory replay in biological and artificial reinforcement learning. Trends Neurosci. 44(10), 808–821 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kamla Nehru Institute of Technology, Sultanpur, Sultanpur, India

    Anuj Singh & Arvind Kumar Tiwari

Authors
  1. Anuj Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arvind Kumar Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anuj Singh .

Editor information

Editors and Affiliations

  1. Assam University, Silchar, India

    Somnath Mukhopadhyay

  2. Assam University, Silchar, India

    Sunita Sarkar

  3. Visva-Bharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

  4. University of Kalyani, Nadia, India

    Jyotsna Kumar Mandal

  5. Assam University, Silchar, India

    Sudipta Roy

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Singh, A., Tiwari, A.K. (2022). A Survey on Computational Intelligence Techniques in Learning and Memory. In: Mukhopadhyay, S., Sarkar, S., Dutta, P., Mandal, J.K., Roy, S. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2022. Communications in Computer and Information Science, vol 1579. Springer, Cham. https://doi.org/10.1007/978-3-031-10766-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10766-5_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10765-8

  • Online ISBN: 978-3-031-10766-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics