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Since 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
by 21 8y ago
Since 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/