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Artificial Neural Network Robot

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jon:
Here's an example CNN circuit controlling a phototropic obstacle-avoiding robot. The "+" neurons constantly send action potentials, causing the motors to move when there is no sensory input. As an example, the robot moves forward until it hits something on the right side. The right contact switch activates, sending an AP to several interneurons which, ultimately, activate a neuron that sends an inhibitory AP to the left motor, causing the robot to turn left. This whole process takes 4 cycles. Similar circuits cause it to follow light. Note some interesting behavior: the robot moves forward in pulses, because there are gaps between action potentials caused by the neurons' refractory periods.

jon:
This is the paper off of which my CNN circuit is partially based off of.


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EDIT: Here is a video of a robot controlled by a genetic algorithm with a population of one.


http://www.youtube.com/watch?v=KHV7fWvnn_0&feature=related

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EDIT2: oops, wrong youtube video, I meant this one:

http://www.youtube.com/watch?v=68AR5WOUxeg&feature=related

Panda:
Houshalter has been there. :)

jon:

--- Quote from: Numsgil on December 28, 2010, 01:45:18 PM ---
--- Quote ---Now, why do you say that neural networks do not imitate biology?
--- End quote ---

They were thought up at a time when little was understood about the brain.  We still don't understand all that much about the brain.  Specifically, there is some evidence that neurons themselves are actually rather sophisticated computing devices.  The simple gate-like nodes in the neural network don't necessarily reflect that.  Really, ANN aren't that interesting compared to other pattern recognition and learning algorithms, but because it has a sexy name, it gets lots of undue attention.

In that a recurrent neural network is Turing complete, I guess you could argue that you just have to take lots of nodes in the ANN in clusters to represent biological neurons (a neural net of neural nets or something like that), but I still think this is a case of gathering a dozen fake plastic trees and calling it a forest.

Second, there are exactly 0 problems (I know of) where ANN outperform other dedicated solutions for a given problem.  Their "strength" is that they can solve a wide variety of problems... badly.

For instance, I've been playing a lot of go in the past year.  The top computer programs still can't beat top level human players.  But they're still pretty strong.  Are these top level programs ANN?  No, they're actually monte carlo simulations.  They play millions of provisional games of go from the current position with fairly random moves and play the move that has the best payback for some given weighting of the imaginary games.  There are lots of other clever techniques like that for every single problem domain I know of.  You have to assume that the brain isn't stupid enough to rely on a bad general purpose algorithm when it could use sophisticated dedicated solutions to various problems.

So it seems incredibly unlikely to me that our brains are actually modeled entirely similarly to a ANN.  Maybe the part of our nervous system that feeds sensory inputs and outputs to and from the brain up through the brain stem works like that.  But the brain "proper" is almost certainly doing something else entirely.  Or think of it this way: if it was a ANN-type thing, wouldn't it make more sense to spread the processing power out along the entire body?  Instead of centralizing it in a central, easy-to-injure location?  That's the raison d'être of the internet, for instance.  You could make an argument for reaction times, etc. for centralizing the brain, but I still think the whole ANN idea is missing the point by a wide angle.  I think that modern multicore CPUs (with pipelines and caches and such) probably more closely models the way the brain works, not because it was trying to, but because it's solving practical general-computing problems which are similar (after a fashion) to the problems that the brain has to solve.

I think the neurons in the brain are like 1 khz processors or something like that.  Nothing to sneeze at!  For some rough numbers, take the estimated processing power from here and the number of neurons from here.  I get 10^14 IPS (simple instructions per second) and 10^11 neurons, which give about 1 khz per neuron.  My guess is that individual neurons do a great deal of work, like a beowulf cluster.


--- Quote ---I hope you don't mind me posting on something a little off-topic, since it isn't that related to DB, but I AM a fan of your work! Let me know if I am bothering you. And thank you so much for your willingness to help, I truly appreciate it.

--- End quote ---

Not at all.  I'm sympathetic to wanting to discuss something that other people either don't understand or don't care about :)  Don't let my grumpy sour-puss attitude put you off.

--- End quote ---

I don't know if I mentioned this yet, but thank you very much for your help! I really appreciate it.

Numsgil:
That's about all the feedback I can give you, btw.  You're well into territory I haven't really explored, let alone thought about.

So best of luck, let us know how it goes.

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