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Cubic millimetre of brain mapped at nanoscale resolution

nature.com

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Re: Cubic millimetre of brain mapped at nanoscale resolution

#31

Post by Terr_ »

nicklecompte wrote: post #14
jamiek88 wrote: post #9

Earlier quoted context omitted.

My god. That is stunning. To think that’s one single millimeter of our brain and look at all those connections. Now I understand why crows can be so smart walnut sized brain be damned. What an amazing thing brains are. Possibly the most complex things in the universe. Is it complex enough to understand itself though? Is that logically even possible?

Crow/parrot brains are tiny but in terms of neuron count they are twice as dense as primate brains (including ours): https://www.sciencedirect.com/science/article/pii/S096098221... If someone did this experiment with a crow brain I imagine it would look “twice as complex” (whatever that might mean). 250 million years of evolution separates mammals from birds.

I expect we'll find that it's all a matter of tradeoffs in terms of count vs size/complexity... kind of like how the "spoken data rate" of various human languages seems to be the same even though some have complicated big words versus more smaller ones etc.

Re: Cubic millimetre of brain mapped at nanoscale resolution

#32

Post by theogravity »

> The brain fragment was taken from a 45-year-old woman when she underwent surgery to treat her epilepsy. It came from the cortex, a part of the brain involved in learning, problem-solving and processing sensory signals.

Wonder how they figured out which fragment to cut out.

Re: Cubic millimetre of brain mapped at nanoscale resolution

#33

Post by adamwong246 »

g4zj wrote: post #10

Is there a name for the somewhat uncomfortable feeling caused by seeing something like this? I wish I could better describe it. I just somehow feel a bit strange being presented with microscopic images of brain matter. Is that normal?

https://hitchhikers.fandom.com/wiki/Total_Perspective_Vortex

Re: Cubic millimetre of brain mapped at nanoscale resolution

#34

Post by marcosdumay »

throwup238 wrote: post #12

> The 3D map covers a volume of about one cubic millimetre, one-millionth of a whole brain, and contains roughly 57,000 cells and 150 million synapses — the connections between neurons. This is great and provides a hard data point for some napkin math on how big a neural network model would have to be to emulate the human brain. 150 million synapses / 57,000 neurons is an average of 2,632 synapses per neuron. The adu…

There's a lot of in-neuron complexity, I'm sure there is some cross-synapse signaling (I mean, how can it not exist? There's nothing stopping it.), and I don't think the synapse behavior can be modeled as just more signals.

Re: Cubic millimetre of brain mapped at nanoscale resolution

#36

Post by cyberax »

throwup238 wrote: post #12

> The 3D map covers a volume of about one cubic millimetre, one-millionth of a whole brain, and contains roughly 57,000 cells and 150 million synapses — the connections between neurons. This is great and provides a hard data point for some napkin math on how big a neural network model would have to be to emulate the human brain. 150 million synapses / 57,000 neurons is an average of 2,632 synapses per neuron. The adu…

On the other hand, a significant amount of neural circuitry seems to be dedicated to "housekeeping" needs, and to functions such as locomotion.

So we might need significantly less brain matter for general intelligence.

Re: Cubic millimetre of brain mapped at nanoscale resolution

#37

Post by scotty79 »

blincoln wrote: post #35

Why did the researchers use ML models to do the reconstruction and risk getting completely incorrect, hallucinated results when reconstructing a 3D volume accurately using 2D slices is a well-researched field already?

Maybe it's not about reconstructing a volume but about recognizing neurons within that volume.

Re: Cubic millimetre of brain mapped at nanoscale resolution

#38

Post by rmorey »

blincoln wrote: post #35

Why did the researchers use ML models to do the reconstruction and risk getting completely incorrect, hallucinated results when reconstructing a 3D volume accurately using 2D slices is a well-researched field already?

The methods used here are state of the art. The problem is not just turning 2D slices into a 3D volume, the problem is, given the 3D volume, determining boundaries between (and therefore the 3d shape of) objects (i.e. neurons, glia, etc) and identifying synapses

Re: Cubic millimetre of brain mapped at nanoscale resolution

#39

Post by greenbit »

g4zj wrote: post #18
greenbit wrote: post #16

Earlier quoted context omitted.

Is it the shapes, similar to how patterns of holes can disturb some people? Or is it more abstract, like "unknowable fragments of someone's inner-most reality flowed through there"? Not that I have a name for it either way. The very shape of it (in context) might represent an aspect of memory or personality or who knows what.

> "unknowable fragments of someone's inner-most reality flowed through there" It's definitely along these lines. Like so much (everything?) that is us happens amongst this tiny little mesh of connections. It's just eerie, isn't it? Sorry for the mundane, slightly off-topic question. This is far outside my areas of knowledge, but I thought I'd ask anyhow. :)

It's kind of feeling a bit like an intruder? There probably is a name for that.

Re: Cubic millimetre of brain mapped at nanoscale resolution

#40

Post by VikingCoder »

blincoln wrote: post #35

Why did the researchers use ML models to do the reconstruction and risk getting completely incorrect, hallucinated results when reconstructing a 3D volume accurately using 2D slices is a well-researched field already?

I'm guessing a registration problem.

If all of the layers were guaranteed to be orthographic with no twisting, shearing, scaling, squishing, with a consistent origin... Then yeah, there's a huge number of ways to just render that data.

But if you physically slice layers first, and scan them second, there are all manner of physical processes that can make normal image stacking fail miserably.

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