Skip to main content
Erschienen in:

23.02.2023

Does Deep Learning Have Epileptic Seizures? On the Modeling of the Brain

verfasst von: Damien Depannemaecker, Léo Pio-Lopez, Christophe Gauld

Erschienen in: Cognitive Computation | Ausgabe 5/2024

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

If the development of machine learning and artificial intelligence plays a role in many fields of research and technology today, it has a special relationship with neurosciences. Indeed, historically inspired by our knowledge of the brain, deep learning shares some vocabularies with neurosciences and can sometimes be considered a brain’s model. Taking the particular example of seizure, which can develop in any biological neural tissue, we question if and how the models used for deep learning can capture or model these pathological events. This particular example is a starting point to discuss the nature, limits, and functions of these models, and we discuss what we expect from a model of the brain. Finally, we argue that a pluralistic approach leading to the integrated coexistence of different models is necessary to study the brain in all its complexity.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
2.
Zurück zum Zitat Richards BA, Lillicrap TP, Beaudoin P, Bengio Y, Bogacz R, Christensen A, et al. A deep learning framework for neuroscience. Nat Neurosci. 2019;22(11):1761–70.CrossRef Richards BA, Lillicrap TP, Beaudoin P, Bengio Y, Bogacz R, Christensen A, et al. A deep learning framework for neuroscience. Nat Neurosci. 2019;22(11):1761–70.CrossRef
3.
Zurück zum Zitat Marblestone AH, Wayne G, Kording KP. Toward an integration of deep learning and neuroscience. Front Comput Neurosci. 2016;10:94.CrossRef Marblestone AH, Wayne G, Kording KP. Toward an integration of deep learning and neuroscience. Front Comput Neurosci. 2016;10:94.CrossRef
4.
Zurück zum Zitat Yan LC, Yoshua B, Geoffrey H. Deep learning. Nature. 2015;521(7553):436–44.CrossRef Yan LC, Yoshua B, Geoffrey H. Deep learning. Nature. 2015;521(7553):436–44.CrossRef
5.
Zurück zum Zitat Tang B, Pan Z, Yin K, Khateeb A. Recent advances of deep learning in bioinformatics and computational biology. Front Genet. 2019;10:214.CrossRef Tang B, Pan Z, Yin K, Khateeb A. Recent advances of deep learning in bioinformatics and computational biology. Front Genet. 2019;10:214.CrossRef
6.
Zurück zum Zitat Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw. 2015;61:85–117.CrossRef Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw. 2015;61:85–117.CrossRef
8.
Zurück zum Zitat Rahwan I, Cebrian M, Obradovich N, Bongard J, Bonnefon JF, Breazeal C, et al. Machine behaviour. Nature. 2019 04;568(7753):477–86. Rahwan I, Cebrian M, Obradovich N, Bongard J, Bonnefon JF, Breazeal C, et al. Machine behaviour. Nature. 2019 04;568(7753):477–86.
11.
Zurück zum Zitat McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics. 1943;5(4):115–33.MathSciNetCrossRef McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics. 1943;5(4):115–33.MathSciNetCrossRef
13.
Zurück zum Zitat Miikkulainen R, Liang J, Meyerson E, Rawal A, Fink D, Francon O, et al. Evolving deep neural networks. In: Artificial intelligence in the age of neural networks and brain computing. Elsevier; 2019. p. 293–312. Miikkulainen R, Liang J, Meyerson E, Rawal A, Fink D, Francon O, et al. Evolving deep neural networks. In: Artificial intelligence in the age of neural networks and brain computing. Elsevier; 2019. p. 293–312.
14.
16.
Zurück zum Zitat Thangavel P, Thomas J, Peh WY, Jing J, Yuvaraj R, Cash SS, et al. Time-frequency decomposition of scalp electroencephalograms improves deep learning-based epilepsy diagnosis. Int J Neural Syst. 2021;31(08):2150032.CrossRef Thangavel P, Thomas J, Peh WY, Jing J, Yuvaraj R, Cash SS, et al. Time-frequency decomposition of scalp electroencephalograms improves deep learning-based epilepsy diagnosis. Int J Neural Syst. 2021;31(08):2150032.CrossRef
17.
Zurück zum Zitat Ullah I, Hussain M, Aboalsamh H, et al. An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst Appl. 2018;107:61–71.CrossRef Ullah I, Hussain M, Aboalsamh H, et al. An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst Appl. 2018;107:61–71.CrossRef
18.
Zurück zum Zitat Sun M, Wang F, Min T, Zang T, Wang Y. Prediction for high risk clinical symptoms of epilepsy based on deep learning algorithm. IEEE Access. 2018;6:77596–605.CrossRef Sun M, Wang F, Min T, Zang T, Wang Y. Prediction for high risk clinical symptoms of epilepsy based on deep learning algorithm. IEEE Access. 2018;6:77596–605.CrossRef
20.
Zurück zum Zitat deAlmeida ACG, Rodrigues AM, Scorza FA, Cavalheiro EA, Teixeira HZ, Duarte MA, et al. Mechanistic hypotheses for nonsynaptic epileptiform activity induction and its transition from the interictal to ictal state-Computational simulation. Epilepsia. 2008 Nov;49(11):1908–24. Available from: https://doi.org/10.1111/j.1528-1167.2008.01686.x. deAlmeida ACG, Rodrigues AM, Scorza FA, Cavalheiro EA, Teixeira HZ, Duarte MA, et al. Mechanistic hypotheses for nonsynaptic epileptiform activity induction and its transition from the interictal to ictal state-Computational simulation. Epilepsia. 2008 Nov;49(11):1908–24. Available from: https://​doi.​org/​10.​1111/​j.​1528-1167.​2008.​01686.​x.
22.
Zurück zum Zitat Depannemaecker D, Ivanov A, Lillo D, Spek L, Bernard C, Jirsa V. A unified physiological framework of transitions between seizures, sustained ictal activity and depolarization block at the single neuron level. J Comput Neurosci. 2022;50(1):33–49. Available from: https://doi.org/10.1007/s10827-022-00811-1. Depannemaecker D, Ivanov A, Lillo D, Spek L, Bernard C, Jirsa V. A unified physiological framework of transitions between seizures, sustained ictal activity and depolarization block at the single neuron level. J Comput Neurosci. 2022;50(1):33–49. Available from: https://​doi.​org/​10.​1007/​s10827-022-00811-1.
26.
Zurück zum Zitat Sapolsky R. Behave : the biology of humans at our best and worst. New York, New York: Penguin Press; 2017. Sapolsky R. Behave : the biology of humans at our best and worst. New York, New York: Penguin Press; 2017.
27.
Zurück zum Zitat Kandel ER, Schwartz JH, Jessell TM, editors. Principles of neural science. 3rd ed. Elsevier; 1991. Kandel ER, Schwartz JH, Jessell TM, editors. Principles of neural science. 3rd ed. Elsevier; 1991.
28.
Zurück zum Zitat Marr D. Vision. MIT Press: The MIT Press; 1982. Marr D. Vision. MIT Press: The MIT Press; 1982.
29.
Zurück zum Zitat Vaughan J, Sudjianto A, Brahimi E, Chen J, Nair VN. Explainable neural networks based on additive index models; 2018. Vaughan J, Sudjianto A, Brahimi E, Chen J, Nair VN. Explainable neural networks based on additive index models; 2018.
31.
Zurück zum Zitat Wan A, Dunlap L, Ho D, Yin J, Lee S, Jin H, et al. NBDT: Neural-Backed Decision Trees; 2021. Wan A, Dunlap L, Ho D, Yin J, Lee S, Jin H, et al. NBDT: Neural-Backed Decision Trees; 2021.
32.
Zurück zum Zitat Ruphy S. Scientific pluralism reconsidered: A new approach to the (dis)unity of science; 2016. Ruphy S. Scientific pluralism reconsidered: A new approach to the (dis)unity of science; 2016.
33.
Zurück zum Zitat Varenne F. From models to simulations. Abingdon, Oxon New York, NY: Routledge; 2019. Varenne F. From models to simulations. Abingdon, Oxon New York, NY: Routledge; 2019.
34.
Zurück zum Zitat Shrestha A, Mahmood A. Review of Deep Learning Algorithms and Architectures. IEEE Access. 2019;7:53040–65.CrossRef Shrestha A, Mahmood A. Review of Deep Learning Algorithms and Architectures. IEEE Access. 2019;7:53040–65.CrossRef
35.
Zurück zum Zitat Rawat W, Wang Z. Deep convolutional neural networks for image classification: A comprehensive review. Neural Comput. 2017;29(9):2352–449.MathSciNetCrossRef Rawat W, Wang Z. Deep convolutional neural networks for image classification: A comprehensive review. Neural Comput. 2017;29(9):2352–449.MathSciNetCrossRef
36.
Zurück zum Zitat Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks; 2014. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks; 2014.
37.
Zurück zum Zitat Lillicrap TP, Santoro A, Marris L, Akerman CJ, Hinton G. Backpropagation and the brain. Nat Rev Neurosci. 2020;21(6):335–46.CrossRef Lillicrap TP, Santoro A, Marris L, Akerman CJ, Hinton G. Backpropagation and the brain. Nat Rev Neurosci. 2020;21(6):335–46.CrossRef
38.
Zurück zum Zitat Whittington JC, Bogacz R. Theories of error back-propagation in the brain. Trends Cogn Sci. 2019;23(3):235–50.CrossRef Whittington JC, Bogacz R. Theories of error back-propagation in the brain. Trends Cogn Sci. 2019;23(3):235–50.CrossRef
41.
Zurück zum Zitat Bickle J. Multiple Realizability. In: Zalta EN, editor. The Stanford Encyclopedia of Philosophy. Summer 2020 ed. Metaphysics Research Lab, Stanford University; 2020. Bickle J. Multiple Realizability. In: Zalta EN, editor. The Stanford Encyclopedia of Philosophy. Summer 2020 ed. Metaphysics Research Lab, Stanford University; 2020.
42.
Zurück zum Zitat Levin M, Pezzulo G, Finkelstein JM. Endogenous bioelectric signaling networks: exploiting voltage gradients for control of growth and form. Annu Rev Biomed Eng. 2017;19:353–87.CrossRef Levin M, Pezzulo G, Finkelstein JM. Endogenous bioelectric signaling networks: exploiting voltage gradients for control of growth and form. Annu Rev Biomed Eng. 2017;19:353–87.CrossRef
43.
Zurück zum Zitat Pezzulo G, Levin M. Re-membering the body: applications of computational neuroscience to the top-down control of regeneration of limbs and other complex organs. Integr Biol. 2015;7(12):1487–517.CrossRef Pezzulo G, Levin M. Re-membering the body: applications of computational neuroscience to the top-down control of regeneration of limbs and other complex organs. Integr Biol. 2015;7(12):1487–517.CrossRef
44.
Zurück zum Zitat Pezzulo G, Levin M. Top-down models in biology: explanation and control of complex living systems above the molecular level. J R Soc Interface. 2016;13(124):20160555.CrossRef Pezzulo G, Levin M. Top-down models in biology: explanation and control of complex living systems above the molecular level. J R Soc Interface. 2016;13(124):20160555.CrossRef
45.
Zurück zum Zitat Floridi L, Chiriatti M. GPT-3: Its nature, scope, limits, and consequences. Mind Mach. 2020;30(4):681–94.CrossRef Floridi L, Chiriatti M. GPT-3: Its nature, scope, limits, and consequences. Mind Mach. 2020;30(4):681–94.CrossRef
46.
Zurück zum Zitat Anderson JR, Matessa M, Lebiere C. ACT-R: A theory of higher level cognition and its relation to visual attention. Hum Comput Interact. 1997;12(4):439–62.CrossRef Anderson JR, Matessa M, Lebiere C. ACT-R: A theory of higher level cognition and its relation to visual attention. Hum Comput Interact. 1997;12(4):439–62.CrossRef
47.
Zurück zum Zitat Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, et al. Recent advances in convolutional neural networks. Pattern Recogn. 2018;77:354–77.CrossRef Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, et al. Recent advances in convolutional neural networks. Pattern Recogn. 2018;77:354–77.CrossRef
51.
Zurück zum Zitat Friston K. The free-energy principle: a unified brain theory? Nat Rev Neurosci. 2010;11(2):127–38.CrossRef Friston K. The free-energy principle: a unified brain theory? Nat Rev Neurosci. 2010;11(2):127–38.CrossRef
52.
Zurück zum Zitat Ullman S. Using neuroscience to develop artificial intelligence. Science. 2019;363(6428):692–3.CrossRef Ullman S. Using neuroscience to develop artificial intelligence. Science. 2019;363(6428):692–3.CrossRef
53.
Zurück zum Zitat Cuthbert BN. The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry. 2014;13(1):28–35.CrossRef Cuthbert BN. The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry. 2014;13(1):28–35.CrossRef
Metadaten
Titel
Does Deep Learning Have Epileptic Seizures? On the Modeling of the Brain
verfasst von
Damien Depannemaecker
Léo Pio-Lopez
Christophe Gauld
Publikationsdatum
23.02.2023
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 5/2024
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-023-10113-y

Weitere Artikel der Ausgabe 5/2024

Cognitive Computation 5/2024 Zur Ausgabe