A Browsable Petascale Reconstruction of the Human Cortex(ai.googleblog.com)
ai.googleblog.com
A Browsable Petascale Reconstruction of the Human Cortex
https://ai.googleblog.com/2021/06/a-browsable-petascale-reconstruction-of.html
34 comments
Hi there, I'm a member of the team at Google responsible for this dataset.
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.
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.
Was this done using the knife edge microscopy technique or was each tissue sliced imaged after mechanically separating it from the rest?
This dataset was collected using physical ~33nm-thick sections that were cut with a diamond knife and stored (and preserved) on a carbon tape before imaging with a multi-beam scanning electron microscope.
You can get the full details in the preprint paper: https://www.biorxiv.org/content/10.1101/2021.05.29.446289v1
You can get the full details in the preprint paper: https://www.biorxiv.org/content/10.1101/2021.05.29.446289v1
The latter. The tissue block is sliced first in the ATUM which collects the sections on tape. These are then mounted on silicon wafers, and imaged with the MultiSEM.
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)
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)
Fascinating work. Does your personal interest in this some from the perspective of (mid/long-term future) WBE?
Hi Jeremy! Thanks for responding, yes that is exactly what I was referring to (I must have mistyped the coordinates).
I didn't copy the URL initially because it is rather long... I should have used tinyurl like you did.
I didn't copy the URL initially because it is rather long... I should have used tinyurl like you did.
The human brain is about 1200 cubic cm [0].
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
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
> The dataset comprises imaging data that covers roughly one cubic millimeter of brain tissue, and includes tens of thousands of reconstructed neurons, millions of neuron fragments, 130 million annotated synapses, 104 proofread cells, and many additional subcellular annotations and structures.
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 :)
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 :)
Yeah, modern CPUs already have hundreds of millions of logic gates and can do calculations much faster than the human brain, yet we need an ridiculous amount of code hours to display some stuff in a screen in a way that barely works.
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?).
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?).
I think much of our understanding of brains is biased by beginning with studying neurons before fully understanding how single cells are able to make complex decisions. Single celled organisms can do incredible things and understanding how they do that using molecular machinery is probably the key to understanding how large networks of them can produce macro scale behaviour by communicating with each other.
Great point. There is ongoing research into whether memory is necessarily a network effect or if single neurons can hold memories. It's looking like they can.
https://www.nature.com/articles/s41598-019-51331-5
https://www.nature.com/articles/s41598-019-51331-5
There are some very sophisticated behaviors displayed by single/low-cell-count micro-organisms. It is something I find very challenging to put into the accepted framework of how cognition works. I think there are some are order-of-magnitude issues that we are missing, probably due to the technology we have available.
Your baseless opinion is 100% correct. Biological brains are not Turing machines and do not have a Harvard or even a Von Neumann architecture.
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.
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.
> My 100% baseless opinion is that animal brains are fundamentally different from computers
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.
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.
> Your brain is not running any software, instead its hardwired to process series of electric signals
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".
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".
Yeah, I read "petascale" and thought, oh, we must be nearly there. Then I saw "one cubic millimeter"! Oh my, we still have a long ways to go.
Does anyone know how big an ant or butterfly's brain is? They do things that are pretty mindblowing in my book with those spec of dust sized brains.
They do!
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.
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.
I think ants may have similar brain size to fruit flys, which we have previously reconstructed. I'm not sure about the butterfly.
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:
https://www.janelia.org/project-team/flyem/hemibrain
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:
https://www.janelia.org/project-team/flyem/hemibrain
I honestly don't know the answer to this, but how large would it be if we tried to do the same thing to a modern CPU? Map our way backwards from its structure instead of the underlying logic?
CPUs are much much more (understandably to us?) structured than the brain. We still don't know if there are repeated canonical cortical micro-circuits. Seems like there should be, but it's still an open research question.
I would assume so as well, but that doesn't mean if we were trying to analyze it this same way, by "building a connectome" of the CPU, the map would also be extremely large, larger than the amount of data necessary to understand/replicate it at least.
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 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.
[deleted]
AFAICT this is a static picture. It doesn't show how synapses change. But merely knowing what is connected to what, at this fine scale, should lead to new insights.
Really excited to see this. I've been following the Licthman Lab's work for a while now.
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.
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.
I hate google blog nowdays
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.
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.
... what exactly are you looking for? A download link for the petabyte dataset?
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.
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.
I immediately went back to Neal Stephenson's "Fall; or, Dodge in Hell" novel. As with so many of his novels, he has extended from the edge of research to ... well, something more, but I don't want to give away too much of the plot.
Cool! And Jeff Hawkins has a new book out, in which he explains aspects of the neocortex and provides a roadmap for ongoing research.
https://www.amazon.com/Thousand-Brains-New-Theory-Intelligen...
The Thousand Brains reference made me immediately think of Minsky's "Society of Mind."[1][2] Minsky's work was quite an insightful perspective on intelligence.
[1] https://en.wikipedia.org/wiki/Society_of_Mind
[2] http://www.jfsowa.com/ikl/Singh03.htm
[1] https://en.wikipedia.org/wiki/Society_of_Mind
[2] http://www.jfsowa.com/ikl/Singh03.htm
The title doesn't quite fit, it's more like hundreds of thousands, as explained in this recent podcast interview.
Guy Kawasaki's Remarkable People https://guykawasaki.com/jeff-hawkins/
Guy Kawasaki's Remarkable People https://guykawasaki.com/jeff-hawkins/
Is there a connectome data set as well? I'm interested to see how small that can get.
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:/...