I thank all the commenters for the valuable feedback. I'm trying to improve this question (which I had long ago -- omg I'm getting old).
Is there a way to simply derive the generalization bounds for the classical perceptron model? More specifically, how to derive the maximum number of patterns that a N-large perceptron can store? And in the case of an NxN network of neurons, how to derive the number of patterns (or memories) and their basins of attractions? Are there simple methods, other than the ones invoking replica theory, as it's done in Gardner's works?
I'm basically referring to the great work of Elizabeth Gardner in this matter. I find that her work is often overlooked in the field of neuroscience, arguably because it is too difficult to understand its mathematical derivation. Yet, her results are so fundamental that many in the field are aware of them.
I couldn't find anywhere a simple explanation of her derivation and the implications of her results, in a terminology that also undergrads could also understand. Of course, I couldn't come up with one myself either. I suppose "Field Theory for Dummies" applied to theoretical neuroscience is what I'm really asking for...