A Complete Electron Microscopy Volume of the Brain of Adult Fruit Fly
cell.com
cell.com
https://ai.googleblog.com/2018/07/improving-connectomics-by-...
One of the authors even stopped by for a chat here about it.
> 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.
We have less than "half the picture" here. Not just weights; also missing electrical synapses, neurotransmitters, etc. We also don't know the spatial scale of neuronal arbor integration. Furthermore these are just the image data, not the complete connectome; people still have to trace circuits by hand in this dataset. Collaborators are starting to crack the segmentation problem, but it is still early days.
Necessary but insufficient class of information!
If anyone is interested you can browse the data live here:https://fafb.catmaid.virtualflybrain.org/?pid=2&zp=131280&yp...
"URL to this view" lets you share URLs to whatever you're looking at.
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in mammals there is pretty good circumstantial evidence that post-synaptic density size correlates with evoked postsynaptic potential, but this hasn't been clearly and directly calibrated yet, and could vary from cell type to cell type
In the above examples dead brains are okay except for electrophysiology, where the brain needs to be alive.
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.
So we should soon be able to control a robot fly?
This is the difference between a von Neumann architecture driven CPU and us.
Why would it be not directly connected on the "OS"?
Or if we can perfectly replicate the brain using our model of deep learning. I'm a skeptic but that would prove it too.
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.
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.
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?
And yet, nobody has managed to do so, despite years of effort.
Why?
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?
There exists opens source simulator for the whole worm (roughly 1000 cells total) http://openworm.org/
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.
So while there is no hard drive with data it does "load data on the networks" in the sense that it puts them into train mode and trains a specific set of actions.
Secondly, from ~16 days after conception the brain (at that point a human embryo is something close to a fish) the brain is trying to learn. At that point, all it can perceive is the mother. So it learns ~8.5 months from the mother before drawing it's first breath. That's also "loading from the harddrive", just with more efficiency.
* one revealing experience I've had is owning an animal and then going to a dog show. The character of dogs of the same breed, even ones that have never seen eachother, ever, in uncannily similar. For horses and cats, I hear it's even worse. I've heard claims that some horse trainers know the exact family relations of a group of horses after observing them for 10 minutes just by seeing behavior differences. But the cats and dogs examples are pretty strong, since those are matching character traits in animals that don't share any close family bond, just the breed, and have never seen eachother. Their mothers and fathers have never seen eachother. And still, they react to strangers exactly the same way, down to whether their head is tilted left or right, how long they look, and many such small things.