I don't know about GMail specifically, but I suspect your approach of using neural networks is the best one given the state-of-the-art in machine learning today.
You can think of naive bayes and a perceptron as roughly equivalent in terms of expressiveness--they're both linear models--but a perceptron is usually better since it can account for correlations between input variables.
As you say, a perceptron is a one-layer neural network, so with a large enough training set, a multi-layer neural network will almost certainly perform better since it can recognize combinations of features that work well together.
Bayesian filtering for spam detection is a good starting point since it's easy to implement, and was very popular in the mid-2000s, but with all the advancements in deep learning since 2006, I'd almost certainly bet on a neural network these days.