I am currently taking a course called "Introduction to Machine Learning with ENCOG 3", and I have a question about how well the Artificial Intelligence (AI) algorithm for a "neural network" corresponds with how an actual neuron works.
x1, x2, etc. are voltage inputs, the wij are weights. The inputs are multiplied by these weights and summed up by the neuron. The neuron then has an "activation function" which then takes the sum of weighted inputs and calculates an output, typically a value between 0 and 1 (or between -1 and 1). You can think of the wij as representing dendrites (a higher weight means a more dense and thus conductive dendrite), and the output of the activation function as the voltage that gets sent down an axon.
The AI neural network algorithm creates a kind of intelligence by modifying the weights (wij shown in the picture).
My first questions is: Is this a good approximation as to how neurons actually work? That is, do our neurons "learn" by changing the weights (dendrite density, conductivity)? Or, is there some other mechanism that is more important (e.g. do neurons learn by changing their activation or summation functions?)
My second question is: If neurons really do learn by changing the density of dendrites, then how fast does this happen? Is this a fast process like DNA replication? Does the neuron quickly generate (or decrease) dendrite density when it receives some kind of biochemical signal that it needs to learn now?
I understand that much of this might not yet be known, but would like to know how well the AI algorithm corresponds with current theories on biological neural networks.