I 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?
Neuron 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.
Directly 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.
When 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.
> 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?
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.
> 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.
And yet, nobody has managed to do so, despite years of effort.
