Dr. Randal Koene: Substrate-Independent Minds (Australian Singularity Summit 2012)
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The first keynote by Dr. Randal Koene at the 2012 Australian Singularity Summit. Dr. Koene presents the concept of substrate-independent minds (SIM) and gives an outline of a feasible approach to SIM by means of whole brain emulation.
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Dr. Randal Koene: Substrate-Independent Minds (Australian Singularity Summit 2012)
- 1. Substrate-Independent Minds 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
- 11. ROADMAP REQUIREMENTS 11
- 12. SCOPE RESOLUTION 12
- 13. 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 13
- 14. STRUCTURAL CONNECTOME 14
- 15. 15
- 16. 16
- 17. FUNCTIONAL CHARACTERIZATION 17
- 18. MOLECULAR TICKER TAPE Kording (Northwestern), Boyden (MIT), Church (Harvard), Koene 18
- 20. WIRELESS MICROPROBES Gomez-Martinez et al. (2009) 20
- 21. 21
- 22. TEAM NETWORK 2um 1um 22
- 23. Carboncopies.org
- 24. B BODY BRAIN
- 25. Thank You carboncopies.org randal.a.koene@carboncopies.org 25
Editor's Notes
- 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)