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FFAI Whole Brain Emulation by Mind Map: FFAI Whole Brain Emulation
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FFAI Whole Brain Emulation


do we know enough?

brain emulation won't work if we don't even know about some important circuit elements

scanning issues (if you don't scan it, you lose it), do we know all the neurotransmitters?, do we know all the channels? about 71 channel subunits from H. sapiens, combining in complicated ways, is the dynamic state of the brain important?

simulation issues (fixable later), are synapses everything? non-synaptic communication: diffusable messengers, glial cells, ephaptic effects, how important are neurogenesis and synaptogenesis?


(separate slides describing the technologies)

generally, a resolution of 5nm x 5nm x 50nm, achievable with electron microscopes

automatic slicing and microscopy, achievable with current technologies

similar to the human genome project in size and scope

do we have the computational resources?

depends on the scale of the simulation

estimates of brain computational power, memory, 10^16 bits (10^10 neurons, 1000 synapses, 34bit ID, 8 bit representation of state; Leitl 1995), 10^20 bits (microtubule memory; Tuszynski, 2006), 10^28 bits (10^11 neurons, 10^4 compartments, 4 dynamic variables and 10 parameters; Malickas, 1996), compare, all human knowledge until 2003: 5 exabyte = 10^19 bits, total Google storage: 1 exabyte (1 million servers x 1 Tb), CPU, 10^14 ops/s (10^10 neurons, 1000 synapses, 10 Hz; Freitas, 1996), 10^17 ops/s (10^11 neurons, 10^4 synapses, 100 Hz, 5 bits/signal; Bostrom, 1998), 10^14 ops/ (retina scale up; Merkle, 1989), 10^18 ops/s (10^11 neurons, 10^4 compartments, Hodkin-Huxley, 1200 FLOPS), compare, current supercomputers: 10^16 ops/s (

more data, current simulations, (lots more), storage, memory, access times, disk storage, processing, MIPS over time (10x = 7.1 years), MIPS/# over time, computer performance per dollar over time, top 500


research cycle

research usually consists of many steps and incremental improvements

it's useful to think about how to organize this cycle so that you achieve continuous improvement (think about it in your own work too)

the research cycle

overlap with other fields

which aspects of brain emulation research are reusable in other fields?, very large scale simulation, environment simulation, supercomputing, fault tolerance, virtual cell, brain, and body models for medical research

who might be motivated to fund some of this work?

what other applications provide economies of scale?

are there related consumer goods that might provide economies of scale? (gaming, household robotics, etc.)

model system

it's a bad idea to start with solving the most complex problem right away; pick a model system instead

C. elegans

302 neurons

eutelic system

simple behavior

well studied

easy genetics

doesn't bite


economic impact is important both for consequences of the research and funding

What would happen if you could "buy a mind", with or without attached arm?, $100m, $1m, $100k, $1000

Who is interested in funding it?




physical handling

imaging, resolution, volume, functional information


image processing, geometric adjustment, data interpolation, noise removal, tracing

scan interpretation, parameter estimation, connectivity identification, synapse identification, cell type identification, databasing


environment simulation

body simulation

distributed computation

fault tolerance

providing adequate resources

(based on Sanders and Bostrom, 2008; based on workshop participants)



physicalism - the mind is a purely physical phenomenon

Turing equivalence - there is no hypercomputation required in the brain


non-organicism - you don't need organic neurons to create a mind

scale separation - there is some scale below which the details of brain function can be abstracted as aggregate properties

scannability - all relevant properties can actually be scanned


brain-centeredness - all you really need for a mind is a brain (plus some I/O)

These are important questions in physics, biology, and neuroscience.

The way we test hypotheses is by experiment, and for many of these hypotheses, emulation is the obvious experiment to do.

scale separation

the nervous system operates at many different scales

how do we simulate this? down to the atom? molecule? neuron?

can we describe the system at different scales? can we abstract functionality at high resolution?

we need at least one cut-off where we stop emulating greater details

observation: microstimulation (individual neurons) can produce macroscopic changes in behavior

but: in software engineering, we have many scales at which we compose software systems, yet changing a single bit somewhere can bring the whole system crashing down

detailed levels of emulation



Sanders and Bostrom: Brain Emulation Roadmap (2008)

(Review Article)


whole brain scanning seems feasible

whole brain emulation is becoming feasible over the next few decades, barring unexpected upsets in the improvement of computer performance


emulation vs simulation

simulation = a computational model of some aspect of a system (not complete)

emulation = a full computational model of an entire system; all relevant properties are modeled

brain emulator = software running on non-neural hardware emulating the brain

mind emulator = brain emulator that succeeds at creating a mind

black box

claim: we do not need to understand the system in order to emulate it

claim: a complete wiring diagram, plus knowledge of the state and properties of individual neurons should be sufficient

Q: is this reasonable? can you translate a piece of software or hardware without understanding it? how do you debug it?

how do we know it's working?

levels of success

levels, parts list (neuron, glia, etc.), complete scan (full 3D scans at high resolution), brain database (combining the above into a wiring diagram), functional brain emulation (emulate the above generically, and produce some general properties of a brain), species-generic brain emulation (produce the full range of normally observed behaviors), social role-fit emulation (the simulation is accepted by others in at least some social roles; e.g. game avatar), mind emulation (the emulation experiences mental states like humans), personal identity emulation (the emulation describes itself as a continuation of the original mind)