6 ms·

Neurons and synapses are incredibly complex. Think you're emulating over 3,000 heterogeneous cores with over half a million links while communication must be lo

by yyyk 4y ago

Neurons and synapses are incredibly complex. Think you're emulating over 3,000 heterogeneous cores with over half a million links while communication must be low-latency-ish. A third of the links seem to join other links and we don't even know what that does. If there's computation there we'll need even more cores.

xyproto 4y ago
It could be simulated slowly at first.
- coldtea 4y ago
  That requires we know how each node behaves and the kind of inputs it has, which we're very very far off from...
- lossolo 4y ago
  To simulate something you need to first know how it works and know all the relevant interactions, this is something that we currently do not understand. Neurons are not equivalent of neural networks we use in computers, they are A LOT more complex with whole groups firing the same time and chemicals regulating neurons all the time and whole topology is plastic, it works in a way we can't model or simulate currently. People hugely overestimate our knowledge about brains.
  - epgui 4y ago
    I'm a biochemist, and I somewhat disagree with this take. Yes, it's true that modelling just a single cell's interactions with its environments is beyond our capacity. But here we're talking about simulating how a brain reacts to signals at a higher level of abstraction: we're studying an "emergent" phenomenon. We don't need to model molecular interactions, and we absolutely can model this using artificial neural networks. This doesn't mean we'll be able to completely accurately model its behaviour, but we should get a lot closer than many HN commenters seem to believe. Biological neurons don't have magical properties, they just have more side-effects.
    - deleted 4y ago
      [deleted]
    - lossolo 4y ago
      I specifically used "relevant interactions" to rule out "modelling just a single cell's interactions with its environments" but who knows how deep you need to go to have an accurate model. > we're studying an "emergent" phenomenon It's like saying that mapping all cells in human organism to addresses in memory will give us emergent human inside computer. But still at abstract layer we just do not have a model of a working brain, we only have maps of neurons, not an actual "algorithm" or a model of all relevant environment interactions needed for a working brain or a set of "abstract instructions" needed for it to work. I think you are over optimistic about current state of our knowledge if we can't even model organism with few thousand neurons accurately. Imagine trying to do that for tens of billions of neurons and trillions of synapses, especially that we know brain doesn't work like an ANN at all[1]. 1. https://www.nature.com/articles/s41583-020-0277-3/ https://www.nature.com/articles/s41583-020-0277-3/
      - groestl 4y ago
        > It's like saying that mapping all cells in human organism to addresses in memory will give us emergent human inside computer. Sounds more like they're saying: a knee is a knee, it doesn't have any magical properties. Build something that bends, and it will behave like a knee. I don't know how true that is, obviously.
        actionfromafar 4y ago
        It's not totally true even for knees. Artificial replacement knees aren't as good as original knees.
        nicoburns 4y ago
        I guess the point is that we might be able to simulate an artificial knee equivalent for neurons. It won’t be as good as a real knee, but it might still be useful.
aperrien 4y ago
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.)

by yyyk 4y ago

xyproto 4y ago

It could be simulated slowly at first.

coldtea 4y ago
That requires we know how each node behaves and the kind of inputs it has, which we're very very far off from...
lossolo 4y ago
To simulate something you need to first know how it works and know all the relevant interactions, this is something that we currently do not understand. Neurons are not equivalent of neural networks we use in computers, they are A LOT more complex with whole groups firing the same time and chemicals regulating neurons all the time and whole topology is plastic, it works in a way we can't model or simulate currently. People hugely overestimate our knowledge about brains.
- epgui 4y ago
  I'm a biochemist, and I somewhat disagree with this take. Yes, it's true that modelling just a single cell's interactions with its environments is beyond our capacity. But here we're talking about simulating how a brain reacts to signals at a higher level of abstraction: we're studying an "emergent" phenomenon. We don't need to model molecular interactions, and we absolutely can model this using artificial neural networks. This doesn't mean we'll be able to completely accurately model its behaviour, but we should get a lot closer than many HN commenters seem to believe. Biological neurons don't have magical properties, they just have more side-effects.
  - deleted 4y ago
    [deleted]
  - lossolo 4y ago
    I specifically used "relevant interactions" to rule out "modelling just a single cell's interactions with its environments" but who knows how deep you need to go to have an accurate model. > we're studying an "emergent" phenomenon It's like saying that mapping all cells in human organism to addresses in memory will give us emergent human inside computer. But still at abstract layer we just do not have a model of a working brain, we only have maps of neurons, not an actual "algorithm" or a model of all relevant environment interactions needed for a working brain or a set of "abstract instructions" needed for it to work. I think you are over optimistic about current state of our knowledge if we can't even model organism with few thousand neurons accurately. Imagine trying to do that for tens of billions of neurons and trillions of synapses, especially that we know brain doesn't work like an ANN at all[1]. 1. https://www.nature.com/articles/s41583-020-0277-3/ https://www.nature.com/articles/s41583-020-0277-3/
    - groestl 4y ago
      > It's like saying that mapping all cells in human organism to addresses in memory will give us emergent human inside computer. Sounds more like they're saying: a knee is a knee, it doesn't have any magical properties. Build something that bends, and it will behave like a knee. I don't know how true that is, obviously.
      - actionfromafar 4y ago
        It's not totally true even for knees. Artificial replacement knees aren't as good as original knees.
        nicoburns 4y ago
        I guess the point is that we might be able to simulate an artificial knee equivalent for neurons. It won’t be as good as a real knee, but it might still be useful.

aperrien 4y ago

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.)