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International Journal of Cheminformatics Review Article

Artificial Intelligence in Cerebellum Activation

  1. A. Mohamed Sikkander Department of Chemistry, Velammal Engineering College
  2. Rajeev Ranjan Department of Chemistry, DSPM University, Ranchi
  3. Sangeeta R Mishra Department of Electronics & Telecommunication, Thakur College of Eng., Mumbai

Abstract

Neuroscience plays a significant function during the progression of artificial intelligence. It provided inspiration for the development of human-like AI. There are two ways that neuroscience encourages us to develop AI systems. Neural networks that replicate human cognition and those that match the structure of the brain are the two objectives. Neural networks, which draw inspiration from the architecture of the human brain, are the engine behind contemporary artificial intelligence systems. Creating and using algorithms that mimic the neural systems present in the human brain is the aim of contemporary AI research. The cerebellum's short- and long-term motor memory formation, the learning of gains in ocular reflex, and the timing of eye blink conditioning are all explained by the liquid-state machine (LSM) model. It integrates the Golgi cells—granule cells—random inhibitory recurrent neural network to a basic perception. The LSM model now incorporates the cerebellar internal model, which supports cognitive function and voluntary movement control. The present state of artificial intelligence (AI) is called deep learning, and it is based on neural network models that began with simple observation. It is thought that the cerebellum is the source of modern artificial intelligence (AI), since the LSM model of the cerebellum represents the brain's implementation of deep learning.

Keywords

References (82)

  1. Ackerman S. Discovering the Brain. Washington (DC): National Academies Press (US); 1992. 2, Major Structures and Functions of the Brain. Available from: https://www.ncbi.nlm.nih.gov/books/NBK234157/
  2. Jimsheleishvili S, Dididze M. Neuroanatomy, Cerebellum. [Updated 2023 Jul 24]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-.https://www.ncbi.nlm.nih.gov/books/NBK538167/
  3. Klein AP, Ulmer JL, Quinet SA, Mathews V, Mark LP. Nonmotor Functions of the Cerebellum: An Introduction. American Journal of Neuroradiology. 2016;37(6):1005-1009. doi:10.3174/ajnr.a4720
  4. Thau L, Reddy V, Singh P. Anatomy, Central Nervous System. [Updated 2022 Oct 10]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK542179/
  5. Miller LE, Holdefer RN, Houk JC. The role of the cerebellum in modulating voluntary limb movement commands. Arch Ital Biol. 2002 Jul;140(3):175–83. PMID: 12173520.
  6. Baud, R., Manzoori, A.R., Ijspeert, A. et al. Review of control strategies for lower-limb exoskeletons to assist gait. J NeuroEngineeringRehabil 18, 119 (2021). https://doi.org/10.1186/s12984–021–00906–3
  7. Wilson, M., Cook, P.F. Rhythmic entrainment: Why humans want to, fireflies can’t help it, pet birds try, and sea lions have to be bribed. Psychon Bull Rev 23, 1647–1659 (2016). https://doi.org/10.3758/s13423–016–1013-x
  8. Schwartz AB. Movement: How the Brain Communicates with the World. Cell. 2016;164(6):1122-1135. doi:10.1016/j.cell.2016.02.038
  9. Pierce JE, Péron J. The basal ganglia and the cerebellum in human emotion. Social Cognitive and Affective Neuroscience. 2020;15(5):599-613. doi:10.1093/scan/nsaa076
  10. Tyng CM, Amin HU, Saad MNM, Malik AS. The Influences of Emotion on Learning and Memory. Frontiers in Psychology. 2017;8. doi:10.3389/fpsyg.2017.01454
  11. Mapelli L, Soda T, D’Angelo E, Prestori F. The Cerebellar Involvement in Autism Spectrum Disorders: From the Social Brain to Mouse Models. International Journal of Molecular Sciences. 2022;23(7):3894. doi:10.3390/ijms23073894
  12. Manto M, Bower JM, Conforto AB, Delgado-García JM, da Guarda SN, Gerwig M, Habas C, Hagura N, Ivry RB, Mariën P, Molinari M, Naito E, Nowak DA, Oulad Ben Taib N, Pelisson D, Tesche CD, Tilikete C, Timmann D. Consensus paper: roles of the cerebellum in motor control--the diversity of ideas on cerebellar involvement in movement. Cerebellum. 2012 Jun;11(2):457–87. doi:10.1007/s12311–011–0331–9. PMID: 22161499; PMCID: PMC4347949.
  13. Baumann, O., Borra, R.J., Bower, J.M. et al. Consensus Paper: The Role of the Cerebellum in Perceptual Processes. Cerebellum 14, 197–220 (2015). https://doi.org/10.1007/s12311–014–0627–7
  14. Prestori F, Mapelli L, D’Angelo E. Diverse Neuron Properties and Complex Network Dynamics in the Cerebellar Cortical Inhibitory Circuit. Frontiers in Molecular Neuroscience. 2019;12. doi:10.3389/fnmol.2019.00267
  15. D’Angelo E, Antonietti A, Casali S, Casellato C, Garrido JA, Luque NR, et al. Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue. Frontiers in Cellular Neuroscience. 2016;10. doi:10.3389/fncel.2016.00176
  16. Negishi, Y., Kawai, Y. Geometric and functional architecture of visceral sensory microcircuitry. Brain StructFunct 216, 17–30 (2011). https://doi.org/10.1007/s00429–010–0294–5
  17. Andrade-Talavera, Y., Fisahn, A. & Rodríguez-Moreno, A. Timing to be precise? An overview of spike timing-dependent plasticity, brain rhythmicity, and glial cells interplay within neuronal circuits. Mol Psychiatry 28, 2177–2188 (2023). https://doi.org/10.1038/s41380–023–02027-w
  18. Gandolfi D, Bigiani A, Porro CA, Mapelli J. Inhibitory Plasticity: From Molecules to Computation and Beyond. International Journal of Molecular Sciences. 2020;21(5):1805. doi:10.3390/ijms21051805
  19. Speranza L, di Porzio U, Viggiano D, de Donato A, Volpicelli F. Dopamine: The Neuromodulator of Long-Term Synaptic Plasticity, Reward and Movement Control. Cells. 2021;10(4):735. doi:10.3390/cells10040735
  20. Appelbaum LG, Shenasa MA, Stolz L, Daskalakis Z. Synaptic plasticity and mental health: methods, challenges and opportunities. Neuropsychopharmacology. 2023 Jan;48(1):113–120. doi:10.1038/s41386–022–01370-w. Epub 2022 Jul 9. PMID: 35810199; PMCID: PMC9700665.
  21. Lőrincz ML, Adamantidis AR. Monoaminergic control of brain states and sensory processing: Existing knowledge and recent insights obtained with optogenetics. Progress in Neurobiology. 2017;151:237-253. doi:10.1016/j.pneurobio.2016.09.003
  22. Bayassi-Jakowicka M, Lietzau G, Czuba E, Steliga A, Waśkow M, Kowiański P. Neuroplasticity and Multilevel System of Connections Determine the Integrative Role of Nucleus Accumbens in the Brain Reward System. International Journal of Molecular Sciences. 2021;22(18):9806. doi:10.3390/ijms22189806
  23. Wolfram Schultz,Getting Formal with Dopamine and Reward,Neuron,Volume 36, Issue 2,2002,Pages 241–263,ISSN 0896–6273, https://doi.org/10.1016/S0896–6273(02)00967–4
  24. Jay A. Blundon, Ildar T. Bayazitov, Stanislav S. Zakharenko,Presynaptic Gating of Postsynaptically Expressed Plasticity at Mature ThalamocorticalSynapses,Journal of Neuroscience 2 November 2011, 31 (44) 16012–16025; doi:10.1523/JNEUROSCI.3281–11.2011
  25. Corlett PR, Honey GD, Krystal JH, Fletcher PC. Glutamatergic Model Psychoses: Prediction Error, Learning, and Inference. Neuropsychopharmacology. 2010;36(1):294-315. doi:10.1038/npp.2010.163
  26. Ong, WY.,Stohler, C.S. & Herr, D.R. Role of the Prefrontal Cortex in Pain Processing. MolNeurobiol 56, 1137–1166 (2019). https://doi.org/10.1007/s12035–018–1130–9
  27. Lerner TN, Holloway AL, Seiler JL. Dopamine, Updated: Reward Prediction Error and Beyond. Current Opinion in Neurobiology. 2021;67:123-130. doi:10.1016/j.conb.2020.10.012
  28. Watabe–Uchida M, Eshel N, Uchida N. Neural Circuitry of Reward Prediction Error. Annu Rev Neurosci. 2017 Jul 25;40:373-394. doi:10.1146/annurev-neuro-072116–031109. Epub 2017 Apr 24. PMID: 28441114; PMCID: PMC6721851.
  29. Slater C, Liu Y, Weiss E, Yu K, Wang Q. The Neuromodulatory Role of the Noradrenergic and Cholinergic Systems and Their Interplay in Cognitive Functions: A Focused Review. Brain Sciences. 2022;12(7):890. doi:10.3390/brainsci12070890
  30. Tanaka H, Ishikawa T, Lee J, Kakei S. The Cerebro-Cerebellum as a Locus of Forward Model: A Review. Frontiers in Systems Neuroscience. 2020;14. doi:10.3389/fnsys.2020.00019
  31. Ishikawa T, Tomatsu S, Izawa J, Kakei S. The cerebro-cerebellum: Could it be loci of forward models? Neuroscience Research. 2016;104:72-79. doi:10.1016/j.neures.2015.12.003
  32. Van Overwalle, F., Manto, M., Cattaneo, Z. et al. Consensus Paper: Cerebellum and Social Cognition. Cerebellum 19, 833–868 (2020). https://doi.org/10.1007/s12311–020–01155–1
  33. Koh M, Markovich B. Neuroanatomy, Spinocerebellar Dorsal Tract. [Updated 2023 Aug 8]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK556013/
  34. Stecina K, Fedirchuk B, Hultborn H. Information to cerebellum on spinal motor networks mediated by the dorsal spinocerebellar tract. The Journal of Physiology. 2013;591(22):5433-5443. doi:10.1113/jphysiol.2012.249110
  35. Akay T, Murray AJ. Relative Contribution of Proprioceptive and Vestibular Sensory Systems to Locomotion: Opportunities for Discovery in the Age of Molecular Science. International Journal of Molecular Sciences. 2021;22(3):1467. doi:10.3390/ijms22031467
  36. Bostan AC, Dum RP, Strick PL. Cerebellar networks with the cerebral cortex and basal ganglia. Trends in Cognitive Sciences. 2013;17(5):241-254. doi:10.1016/j.tics.2013.03.003
  37. Yang GR, Wang XJ. Artificial Neural Networks for Neuroscientists: A Primer. Neuron. 2020;107(6):1048-1070. doi:10.1016/j.neuron.2020.09.005
  38. Shao F, Shen Z. How can artificial neural networks approximate the brain? Frontiers in Psychology. 2023;13. doi:10.3389/fpsyg.2022.970214
  39. Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000 Jun;22(5):717–27. doi:10.1016/s0731–7085(99)00272–1. PMID: 10815714.
  40. Goel, A., Goel, A.K. & Kumar, A. The role of artificial neural network and machine learning in utilizing spatial information. Spat. Inf. Res. 31, 275–285 (2023). https://doi.org/10.1007/s41324–022–00494-x
  41. MontesinosLópez OA, MontesinosLópez A, Crossa J. Multivariate Statistical Machine Learning Methods for Genomic Prediction [Internet]. Cham (CH): Springer; 2022. Chapter 10, Fundamentals of Artificial Neural Networks and Deep Learning. 2022 Jan 14. Available from: https://www.ncbi.nlm.nih.gov/books/NBK583971/ doi:10.1007/978–3–030–89010-0_10
  42. Furquim G, Filho G, Jalali R, Pessin G, Pazzi R, Ueyama J. How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data. Sensors. 2018;18(3):907. doi:10.3390/s18030907
  43. Gurney K. Neural networks for perceptual processing: from simulation tools to theories. Philosophical Transactions of the Royal Society B: Biological Sciences. 2007;362(1479):339-353. doi:10.1098/rstb.2006.1962
  44. Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information ProcessingNikolausKriegeskorteAnnual Review of Vision Science 2015 1:1, 417-446
  45. Ma S, Liu J, Li W, Liu Y, Hui X, Qu P, et al. Machine learning in TCM with natural products and molecules: current status and future perspectives. Chinese Medicine. 2023;18(1). doi:10.1186/s13020-023-00741-9
  46. Choudhary K, DeCost B, Chen C, Jain A, Tavazza F, Cohn R, et al. Recent advances and applications of deep learning methods in materials science. npj Computational Materials. 2022;8(1). doi:10.1038/s41524-022-00734-6
  47. Xu J, Li F, Hou M, Wang P. swAFL: A Library of High-Performance Activation Function for the Sunway Architecture. Electronics. 2022;11(19):3141. doi:10.3390/electronics11193141
  48. Mulindwa DB, Du S. An n-Sigmoid Activation Function to Improve the Squeeze-and-Excitation for 2D and 3D Deep Networks. Electronics. 2023;12(4):911. doi:10.3390/electronics12040911
  49. Dubey SR, Singh SK, Chaudhuri BB. Activation functions in deep learning: A comprehensive survey and benchmark. Neurocomputing. 2022;503:92-108. doi:10.1016/j.neucom.2022.06.111
  50. Vallés-Pérez I, Soria-Olivas E, Martínez-Sober M, Serrano-López AJ, Vila-Francés J, Gómez-Sanchís J. Empirical study of the modulus as activation function in computer vision applications. Engineering Applications of Artificial Intelligence. 2023;120:105863. doi:10.1016/j.engappai.2023.105863
  51. Boven E, Pemberton J, Chadderton P, Apps R, Costa RP. Cerebro-cerebellar networks facilitate learning through feedback decoupling. Nat Commun. 2023 Jan 4;14(1):51. doi:10.1038/s41467–022–35658–8. PMID: 36599827; PMCID: PMC9813152.
  52. Stanca S, Rossetti M, Bongioanni P. The Cerebellum’s Role in Affective Disorders: The Onset of Its Social Dimension. Metabolites. 2023;13(11):1113. doi:10.3390/metabo13111113
  53. Manto M. Mechanisms of human cerebellar dysmetria: experimental evidence and current conceptual bases. J NeuroengRehabil. 2009 Apr 13;6:10. doi:10.1186/1743-0003-6–10. PMID: 19364396; PMCID: PMC2679756.
  54. Buckner RL. The Cerebellum and Cognitive Function: 25 Years of Insight from Anatomy and Neuroimaging. Neuron. 2013;80(3):807-815. doi:10.1016/j.neuron.2013.10.044
  55. Manto M, Serrao M, Filippo Castiglia S, Timmann D, Tzvi-Minker E, Pan MK, et al. Neurophysiology of cerebellar ataxias and gait disorders. Clinical Neurophysiology Practice. 2023;8:143-160. doi:10.1016/j.cnp.2023.07.002
  56. Cabaraux P, Gandini J, Kakei S, Manto M, Mitoma H, Tanaka H. Dysmetria and Errors in Predictions: The Role of Internal Forward Model. International Journal of Molecular Sciences. 2020;21(18):6900. doi:10.3390/ijms21186900
  57. Zhang P, Duan L, Ou Y, Ling Q, Cao L, Qian H, et al. The cerebellum and cognitive neural networks. Frontiers in Human Neuroscience. 2023;17. doi:10.3389/fnhum.2023.1197459
  58. Ashida R, Cerminara NL, Edwards RJ, Apps R, Brooks JCW. Sensorimotor, language, and working memory representation within the human cerebellum. Human Brain Mapping. 2019;40(16):4732-4747. doi:10.1002/hbm.24733
  59. Beuriat PA, Cohen-Zimerman S, Smith GNL, Krueger F, Gordon B, Grafman J. A New Insight on the Role of the Cerebellum for Executive Functions and Emotion Processing in Adults. Frontiers in Neurology. 2020;11. doi:10.3389/fneur.2020.593490
  60. Craig BT, Morrill A, Anderson B, Danckert J, Striemer CL. Cerebellar lesions disrupt spatial and temporal visual attention. Cortex. 2021;139:27-42. doi:10.1016/j.cortex.2021.02.019
  61. Erdal Y, Perk S, Keskinkılıc C, Bayramoglu B, Mahmutoglu AS, Emre U. The assessment of cognitive functions in patients with isolated cerebellar infarctions: A follow-up study. Neuroscience Letters. 2021;765:136252. doi:10.1016/j.neulet.2021.136252
  62. Friedman, N.P., Robbins, T.W. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacol. 47, 72–89 (2022). https://doi.org/10.1038/s41386–021–01132-0
  63. Lin X, Zou X, Ji Z, Huang T, Wu S, Mi Y. A brain-inspired computational model for spatio-temporal information processing. Neural Networks. 2021;143:74-87. doi:10.1016/j.neunet.2021.05.015
  64. Tanaka G, Yamane T, Héroux JB, Nakane R, Kanazawa N, Takeda S, et al. Recent advances in physical reservoir computing: A review. Neural Networks. 2019;115:100-123. doi:10.1016/j.neunet.2019.03.005
  65. Cayco-Gajic NA, Silver RA. Re-evaluating Circuit Mechanisms Underlying Pattern Separation. Neuron. 2019;101(4):584-602. doi:10.1016/j.neuron.2019.01.044
  66. Ankri L, Husson Z, Pietrajtis K, Proville R, Léna C, Yarom Y, Dieudonné S, Uusisaari MY. A novel inhibitory nucleo-cortical circuit controls cerebellar Golgi cell activity. eLife. 2015;4:e06262.
  67. Babadi B, Sompolinsky H. Sparseness and Expansion in Sensory Representations. Neuron. 2014;83:1213–1226.
  68. Valle-Lisboa JC, Pomi A, Mizraji E. Multiplicative processing in the modeling of cognitive activities in large neural networks. Biophys Rev. 2023 Jun 22;15(4):767–785. doi:10.1007/s12551–023-01074–5. PMID: 37681105; PMCID: PMC10480136.
  69. Alammar J (2018) The Illustrated Transformer. https://jalammar.github.io/illustrated-transformer/
  70. Ashida R, Cerminara NL, Edwards RJ, Apps R, Brooks JCW. Sensorimotor, language, and working memory representation within the human cerebellum. Human Brain Mapping. 2019;40(16):4732-4747. doi:10.1002/hbm.24733
  71. Baumann O., Borra R. J., Bower J. M., Cullen K. E., Habas C., Ivry R. B., et al. (2015). Consensus paper: The role of the cerebellum in perceptual processes. Cerebellum 14 197–220. 10.1007/s12311–014–0627–7
  72. Beuriat PA, Cohen-Zimerman S, Smith GNL, Krueger F, Gordon B, Grafman J. A New Insight on the Role of the Cerebellum for Executive Functions and Emotion Processing in Adults. Frontiers in Neurology. 2020;11. doi:10.3389/fneur.2020.593490
  73. Brissenden J. A., Tobyne S. M., Halko M. A., Somers D. C. (2021). Stimulus-specific visual working memory representations in human cerebellar lobule VIIb/VIIIa. J. Neurosci. 41 1033–1045. 10.1523/JNEUROSCI.1253–20.2020
  74. Castellazzi G, Bruno SD, Toosy AT, Casiraghi L, Palesi F, Savini G, et al. Prominent Changes in Cerebro-Cerebellar Functional Connectivity During Continuous Cognitive Processing. Frontiers in Cellular Neuroscience. 2018;12. doi:10.3389/fncel.2018.00331
  75. Chang L, Soomro SH, Zhang H, Fu H. Ankfy1 Is Involved in the Maintenance of Cerebellar Purkinje Cells. Frontiers in Cellular Neuroscience. 2021;15. doi:10.3389/fncel.2021.648801
  76. Cougnoux A, Yerger JC, Fellmeth M, Serra-Vinardell J, Martin K, Navid F, et al. Single Cell Transcriptome Analysis of Niemann–Pick Disease, Type C1 Cerebella. International Journal of Molecular Sciences. 2020;21(15):5368. doi:10.3390/ijms21155368
  77. Cristofori I., Cohen-Zimerman S., Grafman J. (2019). Executive functions. Handb. Clin. Neurol. 163 197–219. 10.1016/B978-0–12–804281–6.00011–2
  78. Erdal Y, Perk S, Keskinkılıc C, Bayramoglu B, Mahmutoglu AS, Emre U. The assessment of cognitive functions in patients with isolated cerebellar infarctions: A follow-up study. Neuroscience Letters. 2021;765:136252. doi:10.1016/j.neulet.2021.136252
  79. Moahamed Sikkander A, Manisankar P , Vedhi C Utilization of sodium montmorillonite clay for enhanced electrochemical sensing of amlodipine,Indian Journal of Chemistry-Section A(IJCA) 55 (5), 571–575DOI: 10.56042/ijca.v55i5.11669
  80. Sivakumar ,R GopalakrishnanP, Abdul RazakMS,Comparative analysis of anti-reflection coatings on solar PV cells through TiO2 and SiO2 nanoparticles,Pigment & Resin Technology 51 (2), 171–177.https://doi.org/10.1108/PRT–08–2020–0084
  81. Mohamed Sikkander A, Nasri NS., Review on Inorganic Nano crystals unique benchmark of Nanotechnology,Moroccan Journal of Chemistry 1 (2), 1–2 (2013) 47–54.https://doi.org/10.48317/IMIST.PRSM/morjchem-v1i2.1892
  82. Mohamed Sikkander A., Bassyouni F, Yasmeen K, Mishra S.R, Lakshmi V.V. Synthesis of Zinc Oxide and Lead Nitrate Nanoparticles and their Applications: Comparative Studies of Bacterial and Fungal (E. coli, A. Niger). J. Appl. Organomet. Chem., 2023, 3(4), 255–267. https://doi.org/10.48309/JAOC.2023.415886.1115