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Kaze

Neural networks

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Just as a experiment I want to try making a simulation / neural network. Out of curiosity dose anyone know how many neurons various animals have, I am thinking my computer can simulate something around sow-bug intelligence. I hope can get something fairly advanced but for now I am just making a simulated bug that can navigate around without crashing into walls. Latter ill see if I can add memory so it can get from point A to B (same as before but can tell if its going in circles). Is there any better way to add memory to a neural network other than having a second map array and set of sensors as this would reduce its flexibility. Secondly real neurons can do minor logical operation in addition to just the trigger threshold, should I bother simulation this and if so how.

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I remember hearing that cochroaches have the least number of neurons in the insect kingdom with 57 (I think). Keep in mind that the interconnections between real neurons in any animal are *far* more complicated than what you get in a neural network, with many biological and physical factors such as chemical emmisions to nearby neurons, EM fields that might be affecting nearby neurons, and possibly even interactions at the quantum level.

Anyway, I doubt you'll need that many neurons for basic navigation. I saw an (old) program on AI that said they trained a single neuron to reverse park a truck.

....says a lot about truckies [grin]

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iv heard of tranning real neurons, even for non speices related tasks like making mouse neurons pilot a aircraft flight simulator, how does this process work?

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As joanusdmentia wrote, there's a difference between an A.I. neural network and how a system of biological neurons works. I'm afraid my neuroscientific knowledge isn't very deep, so I can't really tell you how the brain works (in fact, I'm not sure if even the experts in the field fully understand how neurons interact with each other yet).

But is your objective to experiment with neural networks, or to simulate insect intelligence? I've written A.I. for a robot simulator that makes it act a lot like a cockroach (following walls without crashing into them), and all it used was a simple set of rules.

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its just to experiment with neural networks, i find the concept intresting becouse of how it simulates real intelegence and how its adaptable, i only uses insect as a example to be realistic with the number of neurons (i read it would take about 10^35 itanium proccesors to simulate a human mind with neural nets)

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Artificial neural networks are a very crude immitation of the neural networks found in living things. Real neurons are very complicated and have many, many complicated connections to other neurons, and they communicate through physical means. Artificial neurons are usually simple activation functions that communicate with other artificial neurons by passing along a number.

With the connection as weak as it is, for doing something practical with neural networks it can be better to simply consider them as function approximators. It has been shown that feed-forward neural networks with hidden layers and enough neurons are capable of approximating arbitrary functions. The function a feed forward network represents depends on the number of neurons and the weights between the connections. Then it is just give it some input and the function produces some output.

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