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A Complete Electron Microscopy Volume of the Brain of Adult Fruit Fly
- 21 8y agoSince bio neurons are more complex than "neural network nodes" let's say we dedicate one CPU core per neuron. 100k cores is a pretty standard supercomputer. So we should soon be able to control a robot fly?
- wetpaws 8y agoHardware complexity != logical complexity
- janekm 8y agoI don't quite understand what you mean... is it that the logical complexity of the fly brain is lower than the hardware complexity so it should be possible to replicate the "algorithm" to fly without simulating the hardware of the fly brain?
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- nyrulez 8y agoNeuron is the hardware. We have no idea how the software on it works. The way we model neural networks might be a very different from the operating system of the brain. It's like saying we mapped the x86 chip so we should be able to to simulate all it can do without reverse engineering the software.
- kyberias 8y agoJust copy-pasting the same message here multiple times doesn't make it any more true.
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- sigi45 8y agoAnd software. This is the difference between a von Neumann architecture driven CPU and us. Why would it be not directly connected on the "OS"?
- nyrulez 8y agoDirectly or indirectly connected isn't my point. I was referring to the large amount of electrical activity in the brain and we don't really understand what it actually encodes. Unless we reverse engineer it (which is insanely hard), it's hard to know what's "running" on the hardware. Or if we can perfectly replicate the brain using our model of deep learning. I'm a skeptic but that would prove it too.
- wetpaws 8y agoWhen you emulate NES games, you don't emulate all of the atoms in the NES processor, you just go with instruction set and write down some simple aproximation. You would do the same in biology. c elegans connectome has about 300 neurons. If you would simulate the neurons on a hardware level it would be enormously complex task, you would have to deal with ion channels and quirky stochastic effects, perhaps even some quantum physics simulation. The brain of c elegans is insanely complex. The brain structure, on the other hand, is ridiculously simple. You can just take connection and activation weights and write a simple neural network equivalent in an evening. And it would work about as good as a hardware simulation.
- philipkglass 8y agoYou can just take connection and activation weights and write a simple neural network equivalent in an evening. And it would work about as good as a hardware simulation. Isn't this currently an unsettled question? We don't know if simple approximations of easily observable brain features (like connections) works as well for imitating brains as emulating NES instructions works for imitating the NES. Among other obstacles, we haven't had complete connection maps of large, interesting brains to experiment with. It seems like this fruit fly work may be a stepping stone toward more sophisticated "emulation" experiments.
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- short_sells_poo 8y ago> You can just take connection and activation weights and write a simple neural network equivalent in an evening. And it would work about as good as a hardware simulation. I'm under the impression that this is very far from a settled thing. In effect, we don't know whether the stochastic and quantum effects are important or not. Perhaps that is the secret sauce, and it is not possible to approximate it using "crude" mathematical models we use today. Do you have any links to research that supports your idea?
- dekhn 8y ago
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- janekm 8y agoI think it's fair to say that this map of the fly brain will be extremely useful in validating models of neurons. One slight challenge may be that we are certainly not close to being able to build a physical robot fly that resembles a real one, but perhaps a simulated one could do.
- kyberias 8y agoThe (simulated) fly brain would signal muscles to contract for flight. How would we simulate the muscles? Wings?
- jacquesm 8y agoSimulated wings. Simulated muscles. Why bother hooking it up to anything physical. Even so it won't fly (apologies), it's way more complex than other challenges that are still unsolved in this field.
- buboard 8y agoUnfortunately connectivity is only one property for models. Biorealistic models are lacking sorely in modeling plasticity, which is prerequisite for understanding learning. We 're way behind on mapping that and it is a very complex problem. https://www.ncbi.nlm.nih.gov/pubmed/11127835 https://www.ncbi.nlm.nih.gov/pubmed/11127835
- R0b0t1 8y agoI'm pretty sure that's why he assumed one core per neuron.
- azernik 8y agoGP mentioned understanding, not computational complexity. You need to know how a system works before you can model it.
- ddingus 8y agoModel it correctly, right? Not that I disagree. I do not, but is there not also a path of inference possible? Given "good guess" partially speculative models, would predictability found, should it be found, aid research?
- wavegeek 8y agoArguably the vast majority of the processing power is in the plasticity. That is what enables learning.
- tomrod 8y agoWhat is required to bake in modeling plasticity?
- neuromantik8086 8y agoI'm guessing a better understanding of BDNF and other growth factor proteins would help.
- Jweb_Guru 8y agoWe cannot even model C elegans behavior realistically and it has orders of magnitude fewer neurons. The issue right now is not (solely) computational power.
- nabla9 8y agoC elegans has only 302 neurons and rougly 7,000 connections between the neurons. Neuronal connectivity is similar between individuals. The full connectome has been mapped a long time ago. They are not even spiking neurons and their working is still not fully understood. There exists opens source simulator for the whole worm (roughly 1000 cells total) http://openworm.org/ http://openworm.org/
- nyrulez 8y agoNeuron is the hardware. We have no idea how the software on it works. The way we model neural networks might be a very different from the operating system of the brain. It's like saying we mapped the x86 chip so we should be able to to simulate all it can do without reverse engineering the software.
- russdill 8y agoThat's a huge oversimplification. There isn't some hard drive from where software is loaded onto neurons. A closer model would be saying that the software is the neuron's interconnections and it's activation weights. The neurons are both the hardware and the software.
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- nyrulez 8y agoHow would you be so confident that neuron is both the software and the hardware? Do we actually understand how we have cognition ? There is tremendous amount of electrical activity in the brain (and the entire nervous system). One could argue it's a low dimensional signal like an activor or a much higher abstraction like the software we run on our chips. Just because we use neural network like structures to generate activation weights, how does it follow that's how the brain works?
- neuromantik8086 8y agoFrom a former neuro person: The physical world is not an abstraction- it's material. If you talk to anyone with any reasonable level of knowledge about neuroscience, you aren't going to find anyone who finds the Cartesian dualism you're effectively proposing credible anymore. The only real philosophies that hold their own in the psych/neuro world are reductionism/materialism and biological naturalism. That and lesion studies / patients like H.M. have pretty effectively demonstrated that neurons have both processing and storage functions that aren't necessarily mutually exclusive.
- aavaas 8y agoCan we also map the synaptic strength with this method? If not, I think we only have half the picture; as for brains, configuration of connections are as important as layout of the circuitry.
- aperrien 8y agoLast week a technique was published that might be able to accomplish this in a reasonable amount of time: https://ai.googleblog.com/2018/07/improving-connectomics-by-order-of.html https://ai.googleblog.com/2018/07/improving-connectomics-by-... One of the authors even stopped by for a chat here about it.
- aavaas 8y agoI was talking about synaptic strength (strength of connections between neurons which can vary from +ve to -ve) [0]. There is no mention of synapse in that article. [0] https://en.wikipedia.org/wiki/Synapse https://en.wikipedia.org/wiki/Synapse
- aperrien 8y agoAt the bottom of the article: > Next Steps > We will continue to improve connectomics reconstruction technology, with the aim of fully automating synapse-resolution connectomics and contributing to ongoing connectomics projects at the Max Planck Institute and elsewhere. In order to help support the larger research community in developing connectomics techniques, we have also open-sourced the TensorFlow code for the flood-filling network approach, along with WebGL visualization software for 3d datasets that we developed to help us understand and improve our reconstruction results.
- leemailll 8y agoWhat you quoted is not about synapse strength
- leemailll 8y agoNo, EM can’t offer this type of info. If you are interested take a look at C. Elegans. Done for years but this part is still not clear
- unBrainSurgeon 8y agoGolly, if these aren't some painful comments to read. Dear HN, stick to what your good at.
- sgillen 8y agoPeople are rightfully pointing out that this is only part of the picture, however this is still a really important and useful result! CNNs came by mimicking the layout of a visual cortex, it's very possible other similar breakthroughs could come from better understanding the layout of animal brains.
- davi 8y agoTrue. And this approach continues - the IARPA MICrONs program is trying to do a mm cube of mouse visual cortex: https://www.iarpa.gov/index.php/research-programs/microns https://www.iarpa.gov/index.php/research-programs/microns
- cjohansson 8y agoWill the model start to live when the model is perfect?