Seems two different things are being discussed, sort of top-down, vs bottom-up modelling.
I'm sure we're not far from making a high-level LLM-ish model of behavior based on those extensive studies.
But the topic of discussion is not that, but making a model sufficiently accurate that you "turn the crank" and it yields similar behavior without any priors of what that behavior should be.
To do that, at a minimum, we'd need for each neuron, the profile of responses to each of the neurotransmitters at each synapse, the excitatory/inhibitory effects of each signal, the patterns of how each neuron reacts to those inputs (i.e., receiving a signal from upstream neuron 489327 does not mean that it'll just pass it downstream, but that it'll decide depending on rate of firing, other current excitatory/inhibitory inputs, etc., if and at what rate it'll send the signal downstream), the rate of learning in each of those neurons... and a bunch of other variables, fully modeled.
Then, compute all of those running through the system, and have it take an input like a photo and output the same behavior, from the bottom up, without hints from the behavioral studies.
> But the topic of discussion is not that, but making a model sufficiently accurate that you "turn the crank" and it yields similar behavior without any priors of what that behavior should be.
Change the expression levels of some genes, alter an amino acid here or there, change the input parameters (e.g. make a phenotype that lacks the ability to feel pain), and you can end up with a neuron network that responds subtly, or grossly, differently than another. Put memory into the picture and responses can be learned. Fruit fly behavior permanently changes in response to serious injury, similar to the manner in which humans experience chronic pain. Sure, now this is something that can be modeled, but before the experiment it wouldn't have been modeled.
> After the injury healed, they found the fly's other legs had become hypersensitive. "After the animal is hurt once badly, they are hypersensitive and try to protect themselves for the rest of their lives," said Associate Professor Neely. "That's kind of cool and intuitive."
> "The fly is receiving 'pain' messages from its body that then go through sensory neurons to the ventral nerve cord, the fly's version of our spinal cord. In this nerve cord are inhibitory neurons that act like a 'gate' to allow or block pain perception based on the context," Associate Professor Neely said. "After the injury, the injured nerve dumps all its cargo in the nerve cord and kills all the brakes, forever. Then the rest of the animal doesn't have brakes on its 'pain'. The 'pain' threshold changes and now they are hypervigilant."
Edit: I got confused and didn’t write this comment in the right place.
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I somewhat agree with how you described it as top-down vs bottom-up. I think it’s not exactly how I was framing it, but it’s close enough, and it’s a useful way to think of it.
Even in the rest of your comment you’re taking a bit more of a bottom-up approach relative to what I’m saying: you’d be surprised how much we know about how the brain’s gross organization leads to complex phenomena (pick up the latest edition of Blumenfeld’s clinical neuroanatomy if you want the very-high-level summary).
You can, in principle, achieve a “broadly correct” outcome by doing tissue-level modelling of NNs. It’s surprising how much of the brain is macro components, as opposed to micro, cell-level processing. (Of course I’m handwaving a lot here. I’m afraid anything short of a concrete demonstration is bound to be unsatisfying.)
And yes, modeling at the higher functional level can be very useful; knock out the Wernicke's center and speech goes, visual cortex, vision, etc... So, with a more detailed functional description of each level, we may wind up with a model with useful predictive value.
Tho, that said, how does this approach create a truly robust abstraction from the lower level wetware? Would it provide ability to fully reproduce computations? Would it account for lower-level changes in health, hormones, electrolyte levels, neurotransmitter-active drugs...?
This is a little handwavy, but consider that, in the context of modelling a human brain:
- If you model top-down (at the tissue/functional level like you suggest), you'll be recreating the "broadly correct" kind of computation, and you'll be recreating something that looks and feels like a really really bad, quirky and dumb human brain. But it'll have certain human-like qualities that are maybe even difficult to pin down. These would possibly make a ton of mistakes, but there'd be a lot of human-like biases and mistakes.
- If you throw billions of neurons into a bag, you may be able to train them to perform calculations with a high degree of correctness (eg.: ChatGPT, generative art, modern ADAS systems) but when these make mistakes, the mistakes they make will look extraordinarily stupid to a human (eg.: "a human would never have suddenly steered his car into a brick wall like that").
Both approaches can produce extremely stupid results, but you need the top-down architecture if you want to preserve what makes "the human flavour of intelligence" what it is. (I suppose you could emulate the same result with a big enough bag of neurons, but that sounds very inefficient to me, intuitively.)
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Depending on what you're interested in modelling, you may need to combine multiple approaches, as the brain has multiple layers of emergent properties. I don't think that for most purposes you'd need to go as far as modelling blood contents, but something like it might be required if "embodiment" was an important part of what you'd like to model. There are certain types of things that biological organisms learn particularly fast because they have a physical body that interacts with the real world.
I don't personally believe embodiment is fundamentally required in the model (ie.: I think it's probably possible to emulate the same result if you use a sufficiently large number of neurons), but I think realistically it will be a practical necessity for keeping models and computations as efficient as possible.
What do you actually work on? I'm a synthetic biologist and know a hell of a lot about manipulating pieces of DNA. I know a bit about other branches of lab and academic biology. And I can remember some of the chemistry classes I've taken, usually a bit after the fact.
Are you saying this because you've spent time reading and/or researching fruit fly behavior studies? Or for some other reason?
I currently work as a software/data engineer, but spent 12 years doing fundamental biochem & life science. My MSc was on the characterization of extracellular vesicles in the context of horizontal transfer of information in ccRCC (kidney cancer) cell lines.
I worked closely with one prof (not my PI) who specializes in the study of mitochondrial metabolism in fruit flies, but fruit flies are not my area of expertise. I know just enough to know that there’s an enormous amount of literature on the subject.
