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Machines in minds to
reverse engineer the
machine that is mind
     Randal A. Koene
     Carboncopies.org



                        1
DYSTOPIA




           2
SELECTION




            3
EMPATHY




          4
CHALLENGES




             5
AUGMENTED




            6
BEING




        7
SUBSTRATE-
INDEPENDENT
MINDS




                 WHOLE
                  BRAIN
              EMULATION8
9
ROADMAP
REQUIREMENTS




               10
SCOPE
RESOLUTION




             11
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
STRUCTURAL
CONNECTOME




             13
14
15
FUNCTIONAL
CHARACTERIZATION




                   16
MOLECULAR
TICKER TAPE




      Kording (Northwestern), Boyden (MIT), Church (Harvard), Koene   17
MACHINES
IN MINDS




           Gomez-Martinez et al. (2009)
                                    18
19
TEAM
NETWORK

          2um   1um




                      20
Thank You
    carboncopies.org
randal.a.koene@carboncopies.org



                                  21

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Whole Brain Emulation Roadmap Requirements

Notes de l'éditeur

  1. General concept of demux tree Specific implementation via nanowires, Rudolpho Llinas NYU School of Med, 500nm polymer or platinum wires
  2. General concept of demux tree Specific implementation via nanowires, Rudolpho Llinas NYU School of Med, 500nm polymer or platinum wires
  3. 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
  4. 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
  5. Teams: Hub cell (8um), sensor, stimulation, herding, chains, tag delivery, morphology recording agents (1-2um)