The purpose of a brain is to control homeostasis, motor, and perception functionality. Without a rich world simulation, the brain is just an isolated processor. By modelling at the neuron level, the only inputs can be at the neuron level. That means you have to physically model the entire mouse's body to connect all the inputs.

Even if you had a realistic body simulation, you also need to run the system for an extended period of time. Intelligence is an emergent behavior, and it needs time to work and learn. Since you have to model every single neuron firing at the same time, the speed at which you can run this model is unreasonably slow.

I feel like approaches which slowly build a system of models, each of which simplifies the function of a particular neural region to something computationally tractable is the more realistic approach. In doing so, you develop models which reveal the structure of intelligence.

You have to start somewhere though.

Part of the issue is semantics, what do you consider intelligent? And part is empirical. Insects pop out of a an egg and start doing their 'thing.' Some argue its "intelligence" and others argue is programming of an automaton. Having seen a number of mice go through the whole cycle, from birth to snake chow, my observation was that capabilities developed over time but intelligence appeared to be innate. (which is different than learning, it is in my opinion the ability to learn). I don't know how one my structure an experiment of keeping a mouse effectively in a coma until its body was fully grown, and then measuring its ability to learn, but I did observe that baby mice learned readily things like how to avoid losing track of mom, and how to keep warm. Programming or instinct or something else I don't know.

I do agree that without inputs the brain does not learn but I would not agree that it is not intelligent. So the question might be how fast can it learn. An interesting question might be is it possible to measure the speed of learning independent of a mouse acting on that learning.

my -> might.

I think I've worked on too many large software systems, but my guess?

The human brain is the world's worst spaghetti code and this will be impossible.

The fact you wrote this sentence, let me assume your prefrontal cortex has actually some more abilities :)

We’re thrilled to have been a part of this groundbreaking project. If you’re excited about this sort of science too, drop me a line. We’re hiring talented developers who can push not just the microscope technology but also the super cool bioinformatics just waiting to be mined from these and other projects.

Tim

I can see too how your main challenge is going to be registration/segmentation on such huge datasets but there's plenty of ML/computer vision work and talent next door in Cambridge. Maybe pair-up or present a poster during the incoming New England Machine Learning day at Microsoft Research this May 13th? (Poster deadline April 25) [2]. Good luck with your recruiting!

[1] http://www.tissuevision.com/nihms344616.pdf

[2] http://research.microsoft.com/en-us/events/neml2014/

Is there a torrent available?

And what is the probability my ISP will cut me off for downloading 1.8 PB?

For that matter...I guess I'd need to buy ~600 3 TB hard drives to store it...that would cost $60,000. Plus networking and servers...eep...

Why is it so big?

Suppose you have 75M neurons where each neuron has on average 1000 weighted connections with other neurons. Then you would need 4 bytes per connection to store the address and 4 more bytes to store the weight, for a total of

    (4 + 4 bytes) * 1,000 * 75,000,000 ~ 600 GB

>Comprehensive knowledge of the brain’s wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.

And for those who say that this is not a big deal, it actually is a very big deal. Not only for the connection map of the brain, which may or may not lead to simulating a brain (we are far from that yet), but because we can analyze the distributions of connections and their weights to infer possible functional roles.

Fully documented with plenty of examples and relevant white papers.

Yes, we have a map of 75 million thingies and how they interact in some ways ... but we don't really know what even the interactions of just two neurons "really mean," which parts of their many interactions are the important ones, how they change over time and so-forth.

It does we humans a lot time to figure what an artifact from an alien human civilization does, even a fairly simple one - and that figuring generally involves something like figuring out the "blue print", the "purpose", the "programming" and so-forth of such an artifact. These are all analogies with our own intentional processes.

"Figuring out" the brain even as if it was a human-created artifact seems immensely hard. Yet like DNA, the brain isn't human-created or created at least-all. It just happened, in ways whose understanding will clearly take a lot of unraveling to really understand.

Not sure where I'm going with this aside from awe of the challenge.

In Wolfram alpha and Wikipedia you can read, that the storage capacity of the human brain is around 2-2.5PB.

When I estimate it assuming 10^14 synapses and 24 bytes (weight, connection info, etc.) for each synapse, I come close to 2PB.

I think you can store very efficiently. A neuron or synapse has a huge error rate. So, saving parameters of the synapse as 4 byte floating point number could be overkill. Connection information could be also reduced significantly by an intelligent sorting. E. g. you don't have to store the source index and neurons connect probably more to their neighbors then to neurons far away. Therefore I am wondering, what must be saved in 24 byte. Well, we will know, when we try it some day. At the moment it's just speculation.

How do they actually know they have mapped everything right? How do they test that?

</Accelerando references>

1. what are the immediate applications of having such a dataset? either for the mouse or the brain?

2. once we have the map, will we also have the technology to identify precisely what neurons are lighting up and how information is travelling during certain actions / responses? in short how far are we from a green / yellow / red Google Maps style traffic map for the brain?

2. Functional imaging is advancing, but it's still very hard to capture large-scale neuronal activity. The current state of the art, calcium imaging can visualize activity from one focal plane, not too many neurons at a time, with slow time resolution, and only at the surface. I 'd say we are pretty far from that as it would require some amazing imaging technology.

1. http://www.izhikevich.org/publications/large-scale_model_of_...

2. http://bluebrain.epfl.ch/

We will glean bits and pieces but a detailed functional understanding of an emergent system as large, containing as much feedback (within feedback), and with as enormous a connectivity as any large (or probably even small) mammalian brain is far beyond any reasonable expectation ever.