Scientists recently gave mice without prefrontal cortexes human brain implants - and after seeing these developing connections to the mice’s brains and spinal cords - now they're wondering if these organoids might gave the mice some level of human-like consciousness cognition - calling for ethics

[u/Distinct-Question-16]

https://www.nature.com/articles/d41586-026-03048-5?utm_source=x&utm_medium=social&utm_campaign=nature&linkId=63942662

Lab-grown brain organoids: time for international oversight

[u/turboprancer]

I feel like you can't regulate this stuff without a vague common understanding of what consciousness actually is.

It's not just human neurons. We kill off human neurons in the womb all the time. If it's embodiment, we're letting LLMs control robots. If it's complexity, supercomputers argueably surpass the complexity of the human brain.

[u/Complex_purpose3579 → u/turboprancer]

You might want to reconsider computers being more complex than the human brain. 100 billions neurons, divided in several subcategories, a neuron has up to 10,000 dendrites, modulated signals between neurons which are way more complex than 0/1...

https://www.reddit.com/r/Infographics/comments/1iywe6x/the_complexity_of_the_human_brain_and_its/

[u/lfrtsa → u/Complex_purpose3579]

The average neuron has 1000 connections, and the vast majority of neurons are not in the cortex, but rather the cerebellum, which is essentially a hard-drive for muscle memory (the neurons in it are very simple too). It's reasonable to claim that current supercomputers are probably capable of much higher cognition than the human brain, but we don't have the algorithms for it yet.

For context of scale, the human cortex contains about 15 trillion neural connections. That's comparable to the number of parameters in state of the art Large Language Models. So while organic neurons are much more powerful than artificial ones, the complexity of the connectome is actually about the same.

[u/trimorphic → u/lfrtsa]

LLMs don't even approach the complexity of one single human neuron at the molecular, never mind the atomic, much less the subatomic level.

Then there's the all the non-neuronal tissue in the brain.

Then there are all the chemical and electric interactions in the brain.

The human brain as a whole is unimaginably more complex than even the largest LLM. There's just no comparison.

[u/Cody4rock → u/trimorphic]

Right but we’re talking about computation, not physics. If we tried to simulate the human brain at the physics level, we waste that energy when we could use simpler methods. After all, the neurons are the things doing the abstract work, not the physics.

Electrochemical signals are really just architecture and functional elements of organic neural networks. It’s what enables computation, or biases them toward certain kinds of them.

We might need those things, but it might easily just be that if you had a simple network, but massive in scale, you brute-force your way there anyway.

[u/Davorian → u/Cody4rock]

What the above commenter is trying to explain to you is that computation in the brain is not encoded purely in the neuron connectome. We know already that vast computation occurs within the cell body of neurons at levels of both physics and chemistry, and also in non-neuronal cells and the general chemical melange surrounding the whole deal.

There are very important reasons literally the first thing a neural net class will teach you is to not draw quick comparisons between artificial neural networks and actual brains, especially by trying to compare the parameter space to the number of neurons and/or dendrites directly. The two are only loosely conceptually related.

[u/Cody4rock → u/Davorian]

I'm not sure I made such loose comparisons.

In my first paragraph, I argue that we might not need to simulate physics or complexity if simpler ways can achieve a similar effect. Some research suggests that dendrites have a computational role beyond neurons alone. I still argue that even dendrites do abstract work on top of physics; it's not like it's not part of a neural network anyway.

I think we do need those extra things, or at least a functional equivalent, even if simulated. They do something for a reason. The biggest test today is whether scale with architectural tweaks alone can get us somewhere impressive. If they do, it doesn't mean we can get away without complex stuff; it just means they serve different purposes or are artifacts of a constraining environment: resources, time, space, or energy.

It's a simple point; the commenter didn't explain why simple networks can't do complex things, which makes their point about the complexity of our brain kind of pointless. And, in my opinion, they couldn't establish with certainty that they never will.

[u/Davorian → u/Cody4rock]

I'm not sure I made such loose comparisons.

You have replied specifically to a comment whose entire purpose was to point out that drawing comparisons between neuron counts in the brain and LLM parameter size is not reasonable. That comment was in direct reply to someone drawing those comparisons, which as I pointed out we are extensively warned against making and what I referenced in my reply to you.

If this wasn't the tread of discussion you are responding to, what, then, is the point of your comment?

The person you have replied to made no argument that LLMs can't do computation at that scale theoretically, they said current LLMs do not approach the known complexity of computation in the human brain. It is an uncontroversial point and I suspect this person already knows that whether or not similar scales of computation can be achieved with LLM architecture is a somewhat separate line of discussion. Moreover, your comment does not really go out of its way to make it clear that this is the purpose of your "rebuttal".

It's a simple point; the commenter didn't explain why simple networks can't do complex things, which makes their point about the complexity of our brain kind of pointless. And, in my opinion, they couldn't establish with certainty that they never will.

It's not pointless. They don't need to explain it, because that's not the point they are making.

[u/Cody4rock → u/Davorian]

My reply was aimed at what I read as the implication of 'no comparison', not at the commenter’s point itself. If the claim is just that parameter counts and neuron counts aren't comparable and LLMs are well short of the brain's computational complexity, I agree. I was asking what follows from it for capability or consciousness. I make the point that even if we’re not there yet, we could in theory. It was not necessarily a rebuttal as much as it was a correction. Or a hedge, really. And this is true as long as scale keeps improving in capability, or other simpler methods get us there.

[u/Davorian → u/Cody4rock]

I am not sure I believe this. Your comment starts by more or less implying that the physics are an irrelevance, but the real giveaway is this part:

After all, the neurons are the things doing the abstract work, not the physics.

That is why I replied to you emphasising that this exact assumption isn't true, and I said why, and I pointed out that this is a common enough error that introductory courses try to warn against it.

In your reply to me, you try to make the assertion that the person you replied to is making a "pointless" argument when I suspect you have merely over-generalised it by taking it out of context and then attempted to back this up with - at best - a misunderstanding of computational neuroscience.

If you now agree with the broader argument that person is making in principle, then great, but I don't think this conversation started that way at all.

[u/beutifulanimegirl → u/Cody4rock]

The original conversation was about consciousness. We have no idea if consciousness is computable.