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

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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.

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  1. Saxe A, Nelli S, Summerfield C. If deep learning is the answer, what is the question? Nat Rev Neurosci. 2020;22(1):55–67. Available from:

  2. 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.

    Article  Google Scholar 

  3. Marblestone AH, Wayne G, Kording KP. Toward an integration of deep learning and neuroscience. Front Comput Neurosci. 2016;10:94.

    Article  Google Scholar 

  4. Yan LC, Yoshua B, Geoffrey H. Deep learning. Nature. 2015;521(7553):436–44.

    Article  Google Scholar 

  5. Tang B, Pan Z, Yin K, Khateeb A. Recent advances of deep learning in bioinformatics and computational biology. Front Genet. 2019;10:214.

    Article  Google Scholar 

  6. Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw. 2015;61:85–117.

    Article  Google Scholar 

  7. Reggia JA. The rise of machine consciousness: Studying consciousness with computational models. Neural Netw. 2013;44:112–31. Available from:

  8. Rahwan I, Cebrian M, Obradovich N, Bongard J, Bonnefon JF, Breazeal C, et al. Machine behaviour. Nature. 2019 04;568(7753):477–86.

  9. Jirsa VK, Stacey WC, Quilichini PP, Ivanov AI, Bernard C. On the nature of seizure dynamics. Brain J Neurol. 2014;8:137(Pt 8):2210–30. Available from:

  10. Depannemaecker D, Destexhe A, Jirsa V, Bernard C. Modeling seizures: from single neurons to networks. Seizure. 2021. Available from:

  11. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics. 1943;5(4):115–33.

    Article  MathSciNet  MATH  Google Scholar 

  12. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323(6088):533–6. Available from:

  13. 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. Nøkland A. Direct Feedback Alignment Provides Learning in Deep Neural Networks. arXiv. 2016.

  15. Goodfellow I, Bengio Y, Courville A. Deep learning. MIT press; 2016.

  16. 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.

    Article  Google Scholar 

  17. 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.

    Article  Google Scholar 

  18. 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.

    Article  Google Scholar 

  19. Pumain R, Menini C, Heinemann U, Louvel J, Silva-Barrat C. Chemical synaptic transmission is not necessary for epileptic seizures to persist in the baboon Papio papio. Exp Neurol. 1985;89(1):250–8. Available from:

  20. 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:

  21. Depannemaecker D, Santos LEC, Rodrigues AM, Scorza CA, Scorza FA, deAlmeida ACG. Realistic spiking neural network: Non-synaptic mechanisms improve convergence in cell assembly. Neural Netw. 2020;122:420–33. Available from:

  22. 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:

  23. Tavanaei A, Ghodrati M, Kheradpisheh SR, Masquelier T, Maida A. Deep learning in spiking neural networks. Neural Netw. 2019;111:47–63. Available from:

  24. Nicola W, Clopath C. Supervised learning in spiking neural networks with FORCE training. Nat Commun. 2017;8(1). Available from:

  25. Kolb B, Whishaw IQ. Brain plasticity and behavior. Annu Rev Psychol. 1998;49(1):43–64. PMID: 9496621. Available from:

  26. Sapolsky R. Behave : the biology of humans at our best and worst. New York, New York: Penguin Press; 2017.

    Google Scholar 

  27. Kandel ER, Schwartz JH, Jessell TM, editors. Principles of neural science. 3rd ed. Elsevier; 1991.

  28. Marr D. Vision. MIT Press: The MIT Press; 1982.

    Google Scholar 

  29. Vaughan J, Sudjianto A, Brahimi E, Chen J, Nair VN. Explainable neural networks based on additive index models; 2018.

  30. Yang Z, Zhang A, Sudjianto A. Enhancing explainability of neural networks through architecture constraints. IEEE Trans Neural Netw Learn Syst. 2021;32(6):2610–21. Available from:

  31. Wan A, Dunlap L, Ho D, Yin J, Lee S, Jin H, et al. NBDT: Neural-Backed Decision Trees; 2021.

  32. Ruphy S. Scientific pluralism reconsidered: A new approach to the (dis)unity of science; 2016.

  33. Varenne F. From models to simulations. Abingdon, Oxon New York, NY: Routledge; 2019.

    Google Scholar 

  34. Shrestha A, Mahmood A. Review of Deep Learning Algorithms and Architectures. IEEE Access. 2019;7:53040–65.

    Article  Google Scholar 

  35. Rawat W, Wang Z. Deep convolutional neural networks for image classification: A comprehensive review. Neural Comput. 2017;29(9):2352–449.

    Article  MathSciNet  MATH  Google Scholar 

  36. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks; 2014.

  37. Lillicrap TP, Santoro A, Marris L, Akerman CJ, Hinton G. Backpropagation and the brain. Nat Rev Neurosci. 2020;21(6):335–46.

    Article  Google Scholar 

  38. Whittington JC, Bogacz R. Theories of error back-propagation in the brain. Trends Cogn Sci. 2019;23(3):235–50.

    Article  Google Scholar 

  39. Gauld C, Brun C, Boraud T, Carlu M, Depannemaecker D. Computational models in neurosciences between mechanistic and phenomenological characterizations; 2022. Available from:

  40. Miłkowski M. Computation and Multiple Realizability. In: Fundamental Issues of Artificial Intelligence. Springer International Publishing; 2016. p. 29–41. Available from:

  41. Bickle J. Multiple Realizability. In: Zalta EN, editor. The Stanford Encyclopedia of Philosophy. Summer 2020 ed. Metaphysics Research Lab, Stanford University; 2020.

  42. 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.

    Article  Google Scholar 

  43. 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.

    Article  Google Scholar 

  44. 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.

    Article  Google Scholar 

  45. Floridi L, Chiriatti M. GPT-3: Its nature, scope, limits, and consequences. Mind Mach. 2020;30(4):681–94.

    Article  Google Scholar 

  46. 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.

    Article  Google Scholar 

  47. 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.

    Article  Google Scholar 

  48. Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol. 1952;117(4):500–44. Available from:

  49. Izhikevich EM. Simple model of spiking neurons. IEEE Trans Neural Netw. 2003;14(6):1569–72.

    Article  MathSciNet  Google Scholar 

  50. Goldman JS, Kusch L, Yalcinkaya BH, Depannemaecker D, Nghiem TAE, Jirsa V, et al. Brain-scale emergence of slow-wave synchrony and highly responsive asynchronous states based on biologically realistic population models simulated in The Virtual Brain. bioRxiv. 2020. Available from:

  51. Friston K. The free-energy principle: a unified brain theory? Nat Rev Neurosci. 2010;11(2):127–38.

    Article  Google Scholar 

  52. Ullman S. Using neuroscience to develop artificial intelligence. Science. 2019;363(6428):692–3.

    Article  Google Scholar 

  53. 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.

    Article  Google Scholar 

  54. Mayr E. Cause and effect in biology. Science. 1961;134(3489):1501–6. Available from:

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Correspondence to Damien Depannemaecker.

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Depannemaecker, D., Pio-Lopez, L. & Gauld, C. Does Deep Learning Have Epileptic Seizures? On the Modeling of the Brain. Cogn Comput (2023).

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