4 ms·

I think you may be overestimating the complexity. The better idea is to set up and experiment with different simulation parameters, and see how far they diverge

I think you may be overestimating the complexity. The better idea is to set up and experiment with different simulation parameters, and see how far they diverge from actual observed behavior.

coldtea 4y ago
I think you underestimating the problem. There's no way to even capture and record "observed behavior" at the required level atm...
- epgui 4y ago
  I'm a biochemist and I think you're under-estimating how sophisticated fruit fly behaviour studies are.
  - toss1 4y ago
    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. What am I missing here?
    - epgui 4y ago
      Edit: I got confused and didn’t write this comment in the right place. —- 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.)
      - toss1 4y ago
        Thanks for the recommendation, tracking it down. 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...?
        epgui 4y ago
        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.) --- 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.
  - anonymouskimmer 4y ago
    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?
    - epgui 4y ago
      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.
      - deleted 4y ago
        [deleted]

I think you may be overestimating the complexity. The better idea is to set up and experiment with different simulation parameters, and see how far they diverge from actual observed behavior.

coldtea 4y ago

I think you underestimating the problem. There's no way to even capture and record "observed behavior" at the required level atm...

epgui 4y ago
I'm a biochemist and I think you're under-estimating how sophisticated fruit fly behaviour studies are.
- toss1 4y ago
  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. What am I missing here?
  - epgui 4y ago
    Edit: I got confused and didn’t write this comment in the right place. —- 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.)
    - toss1 4y ago
      Thanks for the recommendation, tracking it down. 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...?
      - epgui 4y ago
        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.) --- 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.
- anonymouskimmer 4y ago
  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?
  - epgui 4y ago
    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.
    - deleted 4y ago
      [deleted]