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A connectomic study of a petascale fragment of human cerebral cortex

vcg.seas.harvard.edu

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Re: A connectomic study of a petascale fragment of human cerebral cortex

#31

Post by nynx »

liamwire wrote: post #30
nynx wrote: post #29

This is fantastic progress. There’s two directions of attack that are necessary to get brain emulation. First, scanning ability needs to advance enough to scan an entire brain with various stains at the decananometer voxel level. Second, we still don’t know enough about how neural cells in the brain function. Just recently, glial cells like astrocytes we’re recognized to have a significant effect on signaling and the…

What’s your field of study for your degree?

I’m in a multidisciplinary engineering major called engineering science.

Re: A connectomic study of a petascale fragment of human cerebral cortex

#32

Post by akomtu »

I feel like the brain complexity is a mirage, a distraction from the fact that even trivial organisms with few neurons demonstrate quite complex and arguably intelligent behavior, and even single cells demonstrate primitive awareness of their surroundings.

Re: A connectomic study of a petascale fragment of human cerebral cortex

#33

Post by KevinGlass »

matthewfcarlson wrote: post #5

There was a story I read once where general AI was unobtainable but computing power continued so simulations of brains was done via processing brain slices. So every AI was just a person who had died with an intact brain that could be preserved. Self driving cars were a thing but it was someone’s grandma.

It's called The Age of Em by Robin Hanson. Here's the wikipedia[1] and a review by a popular blogger[2]. Interesting concept but not a realistic vision of the future.

[1] https://en.wikipedia.org/wiki/The_Age_of_Em [2] https://slatestarcodex.com/2016/05/28/book-review-age-of-em/

Re: A connectomic study of a petascale fragment of human cerebral cortex

#34

Post by LeifCarrotson »

boznz wrote: post #28

I've often wondered about the talk of always needing 10,000 x more processing power before we can emulate something like the human brain, seems to me it should be possible now but running at 10,000th of the speed. I would still be impressed.

Nothing constrains it to a linear relationship, existing compute hardware is essentially single-threaded but neurons are massively parallel. If you need to iterate through an O(n^2) algorithm over 1.4 PB and the brain can do it in O(1) then you still need an absurd level of processing power to get back to 1:10000.

Re: A connectomic study of a petascale fragment of human cerebral cortex

#35

Post by upwardbound »

gilmore606 wrote: post #27
wallacoloo wrote: post #13

Earlier quoted context omitted.

if you can recall the author/title i’d love to look at it! i’m curious how the story deals with the disjointed sensory input in the “revived” agent. e.g. does Grandma experience sight/sound/touch/feel? if not, how does she cope with sudden full-body paralysis/numbness/loss-of-familiar-senses? did she agree to this, or know it was going to happen? and so on. i remember one subplot to a Cory Doctorow book focused on a…

Google 'MMAcevedo' for a horrifying short story take on this concept.

https://qntm.org/mmacevedo

Re: A connectomic study of a petascale fragment of human cerebral cortex

#37

Post by londons_explore »

hyperpallium2 wrote: post #18
KhoomeiK wrote: post #11

It's simply a matter of scaling this slice-scan-render technique up to the entirety of a human brain to simulate human thinking, correct? What're the biggest technological hurdles left?

Seems we can't yet simulate a 302 neuron worm brain (C. elegans) despite having its connectome, so not correct. https://wikipedia.org/wiki/OpenWorm

Do all of these worms use exactly 302 neurons for locomotion? Or is 302 just the number found in a specific worm that was disected?

Re: A connectomic study of a petascale fragment of human cerebral cortex

#38

Post by stef25 »

wallacoloo wrote: post #13
matthewfcarlson wrote: post #5

There was a story I read once where general AI was unobtainable but computing power continued so simulations of brains was done via processing brain slices. So every AI was just a person who had died with an intact brain that could be preserved. Self driving cars were a thing but it was someone’s grandma.

if you can recall the author/title i’d love to look at it! i’m curious how the story deals with the disjointed sensory input in the “revived” agent. e.g. does Grandma experience sight/sound/touch/feel? if not, how does she cope with sudden full-body paralysis/numbness/loss-of-familiar-senses? did she agree to this, or know it was going to happen? and so on. i remember one subplot to a Cory Doctorow book focused on a…

In Sam Harris' podcast he mentions an ethical consideration when trying to create an AI that's conscious: an entity that's fully aware of itself yet stuck in a "box" with no way to get out. The perfect torture machine.

Re: A connectomic study of a petascale fragment of human cerebral cortex

#39

Post by hoseja »

londons_explore wrote: post #37
hyperpallium2 wrote: post #18

Earlier quoted context omitted.

Seems we can't yet simulate a 302 neuron worm brain (C. elegans) despite having its connectome, so not correct. https://wikipedia.org/wiki/OpenWorm

Do all of these worms use exactly 302 neurons for locomotion? Or is 302 just the number found in a specific worm that was disected?

Specifically 302. Each worm has the same brain, down to the cell.

Re: A connectomic study of a petascale fragment of human cerebral cortex

#40

Post by mattkrause »

KhoomeiK wrote: post #11

It's simply a matter of scaling this slice-scan-render technique up to the entirety of a human brain to simulate human thinking, correct? What're the biggest technological hurdles left?

There are interactions that the wiring diagram doesn't account for.

For example, some neurons release "neuromodulators" that diffuse beyond their immediate synaptic partners. Electric fields produced by a fiber bundle can affect activity in neighboring neurons.

Inside the cell, there are all kinds of processes on short (millisecond) and long (day) timescales that alter how the same neuron responds to identical stimuli.

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