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>Is it simulated to the level where you could give it some visual stimulus and observe the actions it is trying to take? No way. First, this mapping doesn't t

• BurningFrog 4y ago
  Another approach is to try emulating different models of neurons/synapses/etc, and find models that act similarity to the real organism. Investing the search space, to invent a name, can sometimes be very powerful.
• falcor84 4y ago
  >Lastly, we don't have the computational ability just yet to simulate even the mapping itself. Could you please explain why not? 548,000 synapses sounds entirely feasible to me.
  • 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/
    • 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.)
  • margorczynski 4y ago
    Wasn't there success in replicating how the neuron modulates a signal with electronics? If we can reproduce the out given any in then that's interchangeable from a system perspective?
• Groxx 4y ago
  For some more context / concrete links: there are ongoing efforts to simulate the C. elegans worm, e.g. https://openworm.org/ https://openworm.org/ , which has ~300 neurons. The actual precision of this model is: nobody knows, because nobody knows precisely what neurons do / what they react to. We know some of it but definitely not all. But, simulating what we do know, you get quite worm-like behavior, despite whatever flaws exist. To get a more perfect simulation, we'd need more perfect knowledge of the chemistry and physics, and lots and lots more computing power. It's something that's continually improving, but a lot of shortcuts have to be made to make it even remotely calculable. That'll always be the case, physics is simply too complicated to both efficiently and accurately simulate.
  • cyberax 4y ago
    OpenWorm is a bad model. To give a computer analogy, you're simulating highly optimized low-level code for Apple ][. That uses all the little hardware-specific tricks and timings to work. A fruit fly brain is a better model to simulate in this regard. It's much more "generic", so there's a hope that we can recapture high-level behavior from it more easily.
    • tsimionescu 4y ago
      > A fruit fly brain is a better model to simulate in this regard. It's much more "generic", so there's a hope that we can recapture high-level behavior from it more easily. This is a completely baseless assumption. It is also moot, since simulating even 300 neurons is beyond our current computational power; simulating 3000 is not going to be possible in the foreseeable future.
      • cutemonster 4y ago
        What makes stimulating 300 that computationally heavy? (Doesn't sound like many?, but I've read that one single neuron needed 100 or so nodes in a deep neural net to simulate. But 300*100 also doesn't sound like many (nodes)?) Edit: some explanations here: https://news.ycombinator.com/item?id=35113498 https://news.ycombinator.com/item?id=35113498 (in this discussion)
• neurohdmi 4y ago
  The first thing you said is correct. To do a proper simulation you would need to gather functional properties of the various cell classes and their synaptic connection, which this study didn't do. (Maybe you can find that information from other lab, I'm not familiar with fly models?) However we definitely have the computational ability to do simulations a fly network. Look at some of the modeling done by the Blue Brain Project or Allen Institute for Brain Science - they do simulations of rat and mice models with hundreds-of-thousands to millions of neurons and exponentially more synapses. 3000 neurons is not that many. If you stuck to non-compartmental point models a 3,000 neuron simulation could probably be ran on a moderately high-end laptop. But as said before, the physical connectome is only part of the information you'd need do any worthwhile simulations.
  • bombolo 4y ago
    But, do we know that they actually model completely accurately?
    • Groxx 4y ago
      We can model some aspects essentially completely - that's basically what this map covers, the "obvious" physical connections. Simplified forms of this can probably be simulated very very quickly. Sometimes that's sufficient. It's not the complete picture though, normally that brain would be in an ever-changing soup of chemicals, which definitely impact behavior... somehow. Simulating that, and even knowing what might be relevant to simulate, will never be complete. Only incrementally better than previous attempts.
    • neurohdmi 4y ago
      If you're lucky, the system you're trying to simulate has good in-vivo recordings. That way you can compare the in-silco models directly to the real ones using either firing rates, LFPs, or other neuronal dynamic. Unfortunately, most of the time that isn't the case. Trying to simulate a 3000 cells and 500K connections of the fly brain is not a computational problem, it's a knowledge one. If you can find functional properties to build a spiking/rates model, and data to compare it too; then it would be feasible (although a lot of work) to build and run simulations on the model. But without that extra info, and only using the physical connectome, there would be very little reason to try to do so.
• stevenjgarner 4y ago
  Synapses of neurons change over time due to synaptic plasticity. This process is responsible for allowing learning and memory. Synaptic plasticity occurs through a combination of changes in the number of synapses and the strength of those synapses. So these brain maps are like a single frame still image frozen in time taken from a video of the changing connectome.