Machines in Minds to Reverse engineer the Machine that is Mind
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TEDxTallinn 2012 talk by Dr. Randal A. Koene. The talk has also been published as an article under the title "A Window of Opportunity". For more information see http://carboncopies.org/a-window-of-opportunity
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Machines in Minds to Reverse engineer the Machine that is Mind
- 1. Machines in minds to reverse engineer the machine that is mind Randal A. Koene Carboncopies.org 1
- 2. DYSTOPIA 2
- 3. SELECTION 3
- 4. EMPATHY 4
- 5. CHALLENGES 5
- 6. AUGMENTED 6
- 7. BEING 7
- 8. SUBSTRATE- INDEPENDENT MINDS WHOLE BRAIN EMULATION8
- 9. 9
- 10. ROADMAP REQUIREMENTS 10
- 11. SCOPE RESOLUTION 11
- 12. EMULATION PLATFORM 80 million ATP per action potential Brain: 20-40W (20-44% of body) Average action potential takes 1.5ms About 4nW per event Supports 7 billion concurrent events Average rate 4-7Hz... 470 billion events/s 12
- 13. STRUCTURAL CONNECTOME 13
- 14. 14
- 15. 15
- 16. FUNCTIONAL CHARACTERIZATION 16
- 17. MOLECULAR TICKER TAPE Kording (Northwestern), Boyden (MIT), Church (Harvard), Koene 17
- 18. MACHINES IN MINDS Gomez-Martinez et al. (2009) 18
- 19. 19
- 20. TEAM NETWORK 2um 1um 20
- 21. Thank You carboncopies.org randal.a.koene@carboncopies.org 21
Notes de l'éditeur
- General concept of demux tree Specific implementation via nanowires, Rudolpho Llinas NYU School of Med, 500nm polymer or platinum wires
- General concept of demux tree Specific implementation via nanowires, Rudolpho Llinas NYU School of Med, 500nm polymer or platinum wires
- You choose a modeling resolution At it and below, functional characterization is key; the components must be simple enough to capture all their relevant behavior Above it, structural characterization is key; the interaction of components and their emergent product; connectome Function-structure in neuronal networks, and hardware/software – both are actually very similar in any other complex system, including computers You can use concurrent functional characterization and low-res structural information (e.g. finding all neurons) to infer functional connectivity – but there is risk of missing latent functions You can reduce the signal dimensions – considering only spatial architectural information (morphology) instead of spatial and temporal information (responses) by combining high-res structural information with inference of “functional type” - but even with a complete library of types and their paramter-related distributions of behaviors, there there is risk that mapping may not be one-to-one and that spatial data errors (e.g. misreadings of component sizes) may be cumulative and may be hard to correct without the addition of locally gathered functional information It is also better in general to rebuild a complex system bit by bit, while carrying out validation that it still works
- Red blood cell diameter 8um. Existing biopassive & bioactive coatings for neural implants. Passes through every capillary to every neuron in the brain. Chip in cell has been done Artificial red blood cell has been done
- Teams: Hub cell (8um), sensor, stimulation, herding, chains, tag delivery, morphology recording agents (1-2um)