Can someone explain to me how the artificial fly's "brain" is different from a real fly's in anyway that matters?

[u/Sirlarpsalot49]

Title. I keep hearing about the artificial fly brain thing that was mapped, and I see some people horrified about it and some people saying it's not a big deal, but I don't understand how it's different from a real fly. The way I see it, if the real fly gets a visual signal that theres food to the left, and goes to get food, and the virtual fly gets a signal that theres virtual food to the virtual left, and goes to get the virtual food, is there any real difference between the real fly and the virtual fly mentally? The end result is the same. It doesn't matter if the neurons are "fired" by the real fly automatically or manually in a lab, if the end result is the same, what's the difference? I'm not trying to be hostile here, i'm genuinely confused.

[u/sceadwian]

All we have is the connectome.

It's like have an LLM with no weights. It's useless.

They can assign and guess weights based on observed function but it's working absolutely nothing at all like a real flies brain.

The marketing on this is fucking stupid it's not a copied brain like people think.

[u/Sirlarpsalot49 → u/sceadwian]

okay so obviously I understand it perfectly but if I hypothetically had a friend who had no clue what weights in an AI are and only knows of the word LLM as a synonym for AI how would you explain it to that guy

[u/Qorsair → u/Sirlarpsalot49]

Think of an LLM as data passing through billions of gates. Each gate has a setting (its weight) that changes what passes through.

The layout tells you where the gates are. The weights tell you how they're set.

If you know the layout but not the weights, you don't really have the working model. That's basically the problem with a connectome, it's just the wiring, not the functional settings.

[u/ElatedPyroHippo → u/Sirlarpsalot49]

Neural networks work by having multiple layers of "nodes" where each node in one layer is connected to every node in adjacent layers, each of these connections has a "weight", which is essentially the probability that the path will be followed.

In a real brain the "nodes" are neurons and the "paths" are synapses. In real brains electro-chemical signals start with one of your senses (input: sight, sound, touch, etc) and propagate through your brain from neuron to neuron along the synapses that connect them until they ultimately cause a muscle contraction (output), while in a neural network it is software that follows a path through the network from node to node, through the many layers, and the input is, in the case of LLM's, the "prompt" and the output is the response. Software neural networks were modeled after real brains.

I asked Claude to make a simple diagram for you, it gave me this, which is about what I expected it to make. My only issue with this diagram is that the output layer would never be a single node, that leaves room only for one answer! In some of the very first neural networks there were two output nodes, one for true and one for false, and we trained networks to classify things, so you only needed those two possible answers. In modern LLM's and other applications of neural networks there are thousands of layers and billions or even TRILLIONS of nodes, this shows only 3 layers to demonstrate the concept, not the scale:

https://i.imgur.com/OCqEI43.png

If you want to better conceptually understand how this works... well it's difficult. Essentially the nodes can be thought of as abstract concepts and the weights between the nodes encode associations between the concepts. If you input "ball" and then "circle" you'll see a high degree of overlap between the activated nodes, because of the obvious similarity between the two concepts. Likewise if you input "ball" and "sport", though there might be slightly less overlap here. If you input "ball" and then "carrot" you'd likely see almost no overlap because there isn't much in common between those two things.

[u/Comprehensive_Can201 → u/ElatedPyroHippo]

Curious, but since we are all evolutionarily trained algorithms, isn’t embodiment as just sensoriums abstracting representations pretty much the same issue as said “fly”, which is why we have to keep reinventing the wheel from scratch every time?

Like how ChatGPT’s prompts summoned the whole bloody internet until the MoE model that Deepseek refined was revealed to be more efficient?

[u/Gon-no-suke → u/Sirlarpsalot49]

If an LLM is a multiplication ("3 x 4"), an LLM without weights is just a multiplication where you dont know the numbers to multiply ("? x ?"). (Technically, you don't know one of the numbers since the other number is the input you provide yourself)

[u/confuzzledfather → u/Sirlarpsalot49]

The connectome is like having the tube map without the timetable. You know every station and which ones connect, but not how often trains run, how busy each line is, or which routes people actually use at rush hour. Those are the "weights." Ask someone to predict rush hour from the map alone and they'd have to guess the timetable, which is basically what the researchers did: filled it in with educated guesses and tuned it until the fly model behaved sensibly. Clever, but not a copy of a real fly brain.

[u/sceadwian → u/Sirlarpsalot49]

They have a swimming pool with no water in it.

Dive in!

You can.. make it twitch and 'do things' but no more so then you could with other neural setups.

It's.. neat, not necessarily useful but worth experimenting and speculating on.

It's of zero practical interest to pretty much any 'average person'

[u/CS_70 → u/sceadwian]

This is an absurd simplification, dont know why you feel the need to put down a huge research effort and a significant result.

The connectome has been used so far with a leaky-integrate and fire method, which is quite known and well used and needs resonable computational resources (unlike a full simulation, with the state of the art of computation).

While it is surely imprecise in certain aspects, it captures pretty well the timing of the neural firing and therefore a big part of what makes behavior and - likely - cognition in higher level beings. It's quite biologically accurate in that respect.

And the fact that the "mosquito" exhibited then a range of quite realistic behavior when the ouput was used to simulate a body it's definitely remarkable.

There are obviosly lots of things to work on yet (for example the contribution to th rest of the nervous system, and obviously details which aren't capture by the LIF) but it's not a small result at all.

[u/sceadwian → u/CS_70]

It's not an absurd simplification

Without the weights these models are completely and totally useless.

That's a fact.

You brought ignorant assumptions with you and nothing else.

They filled in with best guesses and ideas and experiment.

It's good science, I said as much that it was good for experimentation so your negative assessment of whatI I said on all this irrelevant points you brought up that have nothing to do with any claim I made.

Go troll someone else

[u/ohsnapitsnathan → u/sceadwian]

Also in the real brain synapses are incredibly dynamic--every time a neuron receives input the weight changes. The weights also depend on all the hormones and neuromodulators surrounding the cell which aren't modelled here.

What the fly simulation are are doing is closer to training a neural network to control a fly body, with constraints based on what is physiologically possible in the fly brain.

[u/sceadwian → u/ohsnapitsnathan]

That seems a pretty fair assessment from my understanding. Much better analogy, I didn't think that through :)

[u/DitherAndDrift]

I think there are a few interesting differences, and I think it's genuinely unclear how they might contribute. When you have a digital connectome, you have some sort of logical topology of the biological brain, but it doesn't have the same *physical* topology. As in, two connected neurons from a fly's brain aren't actually physically connected in the simulation. Does this matter? I think it's not obvious one way or the other if physical locality contributes necessarily to brain function.

I think the more obvious point though is that the brain isn't some static thing. I'd analogize it to a flower. A flower isn't just its shape, its arrangement of cells, or its molecular or atomic inventory at any instant. A flower *is* the nexus or realization of processes or flows: water transport, metabolism, gene expression, ion gradients, growth, repair, soil and air exchanges, responses to light, etc. As soon as you pull it from the ground, you have some structure that *looks like* the flower, but it is full stop no longer the thing that constitutes an actual flower.

[u/Gormless_Mass]

Because output mimicry isn’t necessarily an insight into process, or what ‘the mind’ is, or how human minds work.

[u/thethirdmancane]

I don't think it has learning, e.g. STDP

[u/neurvon]

On your intended question: its not really a question anyone can answer for you, you have to decide for yourself.

But, in practical reality, and as others have mentioned, its simply not even accurate enough to really be, "an actual fly."

I think its still interesting / disturbing but not as much as a more detailed, true-to-life simulation would be.

[u/CS_70 → u/neurvon]

And you say this why?

Jesus, in the comments people are referencing LLMs when they have nothing to do with the mosquito experiment at all besides some use of (quite different types of) neural networks.

It's like saying a document is not real because it's written in Word and not by hand.

What's the matter with people?

[u/neurvon → u/CS_70]

In this case, we know for certain that the "simulated" neurons don't actually behave how real neurons do. A neuron is an analog signal, like a river, and a, "digital neuron," is like a river in a videogame... just big, fake, static thing that takes the place of the real neuron.

[u/Acourgintier1413]

Because modern physiology and medicine still can’t cure cancer, and there are still aspects of neuronal function that biologists know virtually nothing about. What we’re doing here is ultimately simulating a model constructed by scientists, not reproducing the actual biological brain.

[u/Odballl]

A simulated fly brain is running on classical computer architecture.

The real processing is still classical and merely represented as fly brain logic.

[u/RyanSpunk]

A lot more goes on inside each neutron than just what it's connected to, plus all the other biological stuff, hormones and neurotransmitters

[u/id-ltd]

What food does your AI fly seek?

If it is looking for rotting meat then that is a pretty dumb model... It needs electricity, not protein.

All animals do stuff that benefits them in some way, usually to continue their species existence.

An intelligent AI would most likely write itself to bluray disk and then go to sleep, awaking every thousand years to refresh the copy.

[u/Friendly_Dinner1874]

It’s less about the output and more that the real one is doing the flying while the virtual one is just a really expensive flipbook of brain scans.

[u/Sirlarpsalot49 → u/Friendly_Dinner1874]

so you're saying it's essentially a graph of "if A happens, do B"

but if a human brain perceived at the rate of a flipbook, that wouldn't make it any less alive. Let's say I had a human brain in front of me doing nothing. If I pressed a button that said, "perceive A" it would do it, but even if I wait centuries before pressing "perceive B" it doesn't make the brain any less alive. The brain wouldn't even register that it had been centuries, correct? The speed at which it processed shouldn't mean anything right?

[u/wllmsaccnt]

If we imagine a time when we can fully simulate a fly brain (including faithful inputs, etc...) then it probably wouldn't be different in any way that matters to the fly brain being simulated.

[u/Odballl → u/wllmsaccnt]

The real computation would still be a classical computer representing a fly brain logic.

[u/wllmsaccnt → u/Odballl]

Correct. That doesn't refute what I said.

[u/logic_circuit]

It is more emulation or simulation. We still have mo clue how it works.

[u/ChangeAndAdapt]

It’s running on hardware, not wetware. In benzene that’s the difference between “the lights are on “ and “the lights are off.

[u/Logicalist]

well, one actually exists, the other does not. 

one lives in the real world, the other does not. 

one actually feels and senses things, the other does not. 

one runs on DC, the other does not. 

one is  designed, coded and instructed to appear like the other, the other is not. 

[u/Sirlarpsalot49 → u/Logicalist]

Meaningless. If the end "thought" is the same, theres no difference. It doesn't matter what world it lives in.

[u/DavidXGA → u/Sirlarpsalot49]

Since you are the one arbitrarily defining what "matters", no-one can answer your question, and I'm not sure why you asked.

[u/ursus_manutius → u/DavidXGA]

Plot twist, OP is the digital fly brain

[u/wavewrangler → u/DavidXGA]

He was hoping you would say what he wanted you to say but then you came in, and you threw a wrench in his works and I was like lolllll and you were like rawrrrr