ANN typically stands for artificial neural network, not analog neural network. There is a very different term for an analog electronic neuron simulation.
I never even mentioned artificial neural networks, jeez. Did you even click the link? Do you think it's a rickroll or something?
no, it's worse, it's a popular science article without any explanation whatsoever about what the researchers actually DID.
Okay, okay. I get that you were talking about artificial neural networks. Here's what I'm getting at. The OP article said
"The ability of robots to orientate, escape predators, and even cooperate is particularly remarkable given that they had deliberately simple genotypes directly mapped into the connection weights of neural networks comprising only a few dozen neurons."
While the link I provided says these other guys have produced a chip that has
200,000 neurons linked up by 50 million synaptic connections, the chip is able to mimic the brain's ability to learn more closely than any other machine.
Please tell me you can see why I mentioned this.
well yes, I see why you mentioned this.
and I don't like what I see.
because you seem to imply that more neurons = smarter AI.
I tried to explain in my previous post, but you;re really stuck on your concept of what you think are "neural networks".
see, the things that consist of a few dozen neurons in the robot controllers are completely different things
than the stuff in the 200,000 neuron simulation.
for starters, apart from some superficial similarities, the neurons these two systems consist of aren't even the same things, so it doesn't really make any sense to compare them.
the robot controller neural nets are the pattern recognition algorithms I somewhat explained in my previous post, which are commonly known as Artificial Neural Networks, abbreviated as ANNs. using the abbreviation for something else is really confusing. [and yes it is a pretty standard abbreviation, just check how many scientific AI symposia there are with ANN in their title, and notice it always means "artificial"]
now the 200,000 neuron thing. while probably really impressive, as far as I've understood these things are only useful from a sort of neuro/bio/physiological point of view. they're basically bruteforce simulating parts of the brain. usually rat brains, it seems. now these things may do some kind of pattern recognition, if you try hard enough, but that's not really what they're made for, and I wouldn't even know if they'd be any better at it than the (mathematically proven) traditional algorithms.
no they are used for completely different kinds of research. think of it more like a dummy. basically they got this simulation of a part of the brain (afaik they got up to a beam of synapses in the cortex by now) which is built to mimic the physiological/biological original as close as possible. yep, that is including differential equations to model the reaction and diffusion of neurotransmitter chemicals. the cool thing about this, is if you want to test it, and/or figure out how it works, you don't need to cut open a rat every time, and most importantly, you can look at it while it's running.
and yes, those things do exhibit aspects of learning. but probably not the kind of reasoning-learning you are thinking of. that's a bit too high level for a beam of synapses in a rat's cortex. actually I wonder if rats can even reason? anyway that;s not the point, the kind of learning it displays is more like a few levels below classic pavlov conditioning. neurons that get excited more often, get stronger pathways and therefore excite even more often, and that's basically the building block for learning.
but that doesn;t make a cool popular science article. and that is why, if you're really serious about this stuff, you should avoid those articles and click through until you find something written in LaTeX, with formulas in it, that gives you the deep rundown of what they actually do instead of what the scientific journalists make of it.
anyway if you want to make a cool AI like what you're aiming at, i'm not saying it can't be done, but I think it's kind of cumbersome to make a simulation that low level. if you want to replicate the biological functions so that you can learn more about our own brains, sure. but if you just need the behaviour, you're wasting resources on accuracy that is unnecessary. it's already a computer, after all. you could jam much more artificial neurons into a chip if you took some liberties and they dont have to function exactly numerically like biological neurons. after all, the intelligence is not in the matter that it's made of, but in the patterns that appear/emerge in that matter, right?