A Browsable Petascale Reconstruction of the Human Cortex
ai.googleblog.com
ai.googleblog.com
For example, at x, y, z = 234004, 246203, 2436 (you can manually enter these in the corresponding coordinate boxes on the top left).
Edit: should have added that the above coordinates are for the online dataset browser [0]
[0] https://h01-release-dot-neuroglancer-demo.appspot.com/#!gs:/...
I don't see an artfact at exactly those coordinates, but is this nearby location what you are referring to:
tinyurl.com/4rpas73s
That appears to be a knife mark. This dataset was assembled from over 5000 tissue sections each cut at a thickness of just 30 nanometers using a device called an ultramicrotome. Tiny imperfections can develop in the diamond knife used by the ultramicrotome, which unfortunately can result in artifacts like that. Fortunately such artifacts are unlikely to affect the same location on multiple consecutive sections, and our segmentation algorithms are robust enough to segment through those locations.
By the way, one cool thing about the viewer, neuroglancer, is that you can just copy the URL to link directly to any particular view.
Our preprint at https://www.biorxiv.org/content/10.1101/2021.05.29.446289v1 has some more details about the process, including figures illustrating the ATUM and the tissue block.
(I'm one of the authors of the paper)
You can get the full details in the preprint paper: https://www.biorxiv.org/content/10.1101/2021.05.29.446289v1
I didn't copy the URL initially because it is rather long... I should have used tinyurl like you did.
So you would need about a million of these datasets to fully image the brain, or a zettabyte. This is about as much information as traveled through the Internet in 2015 [1].
[0] https://en.wikipedia.org/wiki/Brain_size [1] https://www.wolframalpha.com/input/?i=zettabyte
On one hand it is interesting to know what percent of 130 million synapses changes on second by second bases. On another hand some believe and argue we can approximate human level dynamic behavior using GPT and a like models, in like 10 years :)
But their lifespan is way shorter and their number is way higher; which means way more feedback from darwinistic evolution.
To me it means their 'intelligent' behavior should be much more a function of being hardcoded in the neuron layout at the genetic level than being in a generic intelligent meat processor at an emergent level.
Much like ASICs vs CPUs.
You can check out a complete fruit fly brain here:
https://fafb-ffn1.storage.googleapis.com/landing.html
Or half of a fruit fly brain that has been more fully reconstructed:
My core point is that the size of this dataset is amazing but, possibly, unrelated to how close or how far away from understanding the brain's behavior.
My 100% baseless opinion is that animal brains are fundamentally different from computers, although they might share some characteristics (like, we can do maths right?).
You are correct in that opinion.
I’m not or specialist in the field, but FWIW key difference between neural circuit vs processor is that former is specialised and analog while latter is programmable and digital.
Your brain is not running any software, instead its hardwired to process series of electric signals („trains”). Neurons can be used to model logic gates, but nature uses different approach where there are „dumb” neurons that just transmit signals and then there are „functional” neurons that emit signal only if certain conditions have been met, be it right frequency of electric signals in „electric train”, current chemical state of neuron or number of received signals from other neurons at given time.
At some levels, you could trivially describe a CPU this way too.
I would be wary of any level of confidence about "what brains do".
If I understand it correctly, biological brains are essentially associative memory machines where memory and computation are intertwined into one physical structural system.
In short: To think is to remember. And to remember is to think.
We need this for every tissue! This should be some kind of X-prize. I want sub-cellular 3d models of all the tissues. If we look closely enough, we might discover a thing or two.
Its like they try to make the awesome data you came to see hard to get to or even find if it really exists. There are 20+ links in that release with ZERO direction as to what one is the headline data I came to see.
5 links deep before you even get to anything headline relevant.
If I click the link for '"H01" dataset', I get https://h01-release.storage.googleapis.com/landing.html, which is a sparse page that includes a link to "Instructions and examples", which is https://h01-release.storage.googleapis.com/data.html, which has a long list of what appear to be URLs you can use to access the data if you are already familiar with the APIs they apparently use for programmatic access to large datasets.
Guy Kawasaki's Remarkable People https://guykawasaki.com/jeff-hawkins/