Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks
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Transcript of Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks
Whole Bra in S imulat ions and the Discrepanc y/Similar i ty betweenAr t i f ic ia l & Natural Neural Networks
1st Deep Learning Club SeminarTuesday, 11th October 2016
Guillaume Dumas, Human Genetics & Cognitive Functions
Introduction
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GoogLeNet, a 22 layers deep network
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“It’s not a human move.I’ve never seen a human play this move.
So beautiful.”Fan Hui, Go European champion
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IBM Neuromorphic Computer TrueNorth
DARPA SyNAPSE Program Plan
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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
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2 main differences:
Structure : redundancy
Dynamics : Evolution vs. Design
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Felleman & Van Essen (1991) asimovinstitute.org/neural-network-zoo/
. . .
Part 1
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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
. . .
Izhikevich & Edelman, PNAS 200812 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11
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“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
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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 TimingDependent Plasticity. Masquelier & Thorpe, PLoS Comp Biol 2007
Part 2
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Dumas et al., PLoS ONE 2010
Dumas et al., PLoS ONE 201220 • 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)
Dumas et al., PLoS ONE 201221 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11
Normal Shuffle
Dumas et al., PLoS ONE 201222 • Guillaume Dumas • Whole Brain Simulations and the Discrepancy/Similarity between Artificial & Natural Neural Networks • 2016/10/11
FFT
Brai
n Ar
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igna
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Cortical Level Scalp Level
C in
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G S
igna
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Dumas et al., PLoS ONE 2012
Real connectivity facilitate inter-brain synchronization
Residual synchronization
Information exchanged between the two virtual brains
Inte
r-br
ain
sync
hron
izat
ion
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Kelso, Dumas, & Tognoli, Neural Networks 2013
Expe
rimen
tal
Com
puta
tiona
l
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Dumas et al. « The Human Dynamic Clamp » PNAS 2014
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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.”
Conclusion
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19361950
1952
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”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
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