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Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks

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Presentation given at the 1st Deep Learning Club Seminar, October 11th, 2016

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Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks

  1. 1. Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks 1st Deep Learning Club Seminar Tuesday, 11th October 2016 Guillaume Dumas, Human Genetics & Cognitive Functions
  2. 2. Introduction 2 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11
  3. 3. 3 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 GoogLeNet, a 22 layers deep network
  4. 4. 4 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 “It’s not a human move. I’ve never seen a human play this move. So beautiful.” Fan Hui, Go European champion
  5. 5. 5 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 IBM Neuromorphic Computer TrueNorth DARPA SyNAPSE Program Plan
  6. 6. 6 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11
  7. 7. 7 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 “a nerve cell is more than a single basic active organ (…) Thus, all the complexities referred to here may be irrelevant, but they may also endow the system with a analog character, or with a ”mixed” character.” Von Neumann (1958) The Computer & the Brain
  8. 8. 8 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 2 main differences: Structure : redundancy Dynamics : Evolution vs. Design
  9. 9. 9 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 Felleman & Van Essen (1991) asimovinstitute.org/neural-network-zoo/ . . .
  10. 10. Part 1 10 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11
  11. 11. 11 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 Plasticity — Going beyond backpropagation Connectivity — From weight sharing to recurrent networks Astrocytes — Managing multiple time scales Body — Convenient to get its own training set! Oscillations — Time, attention, & subthreshold computing . . .
  12. 12. Izhikevich & Edelman, PNAS 2008 12 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11
  13. 13. 13 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11
  14. 14. 14 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 “The dirty secret is that we don’t even understand the nematode C. Elegans, which only has 302 neurons” Christof Koch, Allen Brain Institute Chief Scientific Officer “There is a lot of benefits for each neuroscientist because we have now a new Atlas, we can use supercomputers, we can proof our models, a Neurorobotics Platform, have new simulation tools and so on.” Katrin Amunts, JULICH SP2 Leader
  15. 15. 15 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11
  16. 16. 16 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity. Masquelier & Thorpe, PLoS Comp Biol 2007
  17. 17. Part 2 17 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11
  18. 18. 18 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11
  19. 19. 19 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 Dumas et al., PLoS ONE 2010
  20. 20. Dumas et al., PLoS ONE 2012 20 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 + x 2 = Large-scale, anatomically detailed models of the brain allow to perform experiments that are impossible (physically or ethically)
  21. 21. Dumas et al., PLoS ONE 2012 21 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 Normal Shuffle
  22. 22. Dumas et al., PLoS ONE 2012 22 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 FFTBrainAreaSignals Cortical Level Scalp Level Cintra CintraFFTEEGSignals
  23. 23. 23 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 Dumas et al., PLoS ONE 2012 Real connectivity facilitate inter-brain synchronization Residual synchronization Information exchanged between the two virtual brains Inter-brainsynchronization
  24. 24. 24 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 Kelso, Dumas, & Tognoli, Neural Networks 2013 ExperimentalComputational
  25. 25. 25 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 Dumas et al. « The Human Dynamic Clamp » PNAS 2014
  26. 26. 26 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 Dumas et al. « The Human Dynamic Clamp » PNAS 2014 ”The Turing test implies only that judges are unable to tell if an agent is a human or a machine, and as such says nothing about the genuineness of the path toward that decision. Here, the Human Dynamic Clamp is a tool to test hypotheses and gain understanding about how humans interact with each other as well as with machines. In the HDC paradigm, exploration of the machine’s behavior may be viewed as an exploration of us as well.”
  27. 27. Conclusion 27 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11
  28. 28. 28 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 1936 1950 1952
  29. 29. 29 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 ”Unless our methods can deal with a simple processor, how could we expect it to work on our own brain?” Jonas & Kording 2016 Lesion method Spike trains recordings Local field potential recordings
  30. 30. 30 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 Top-Down (SemioticalView) Bottom-Up (InformationalView) Source: lkm.fri.uni-lj.si
  31. 31. 31 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 Top-Down (SemioticalView) Theory Hypothesis Experiment Data Pattern Model Bottom-Up (InformationalView)
  32. 32. 32 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11 Top-Down (SemioticalView) Bottom-Up (InformationalView) Models
  33. 33. 33 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11
  34. 34. Thanks for your attention gdumas@pasteur.fr – Extrospection.eu – @introspection

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