I was talking with a colleague today who is taking a business-oriented data mining course, and there was a list of neural network books recommended by the instructor. It was fascinating looking at the books in the list because I didn't know several of them. When I examined several of the recommended books on amazon.com, I found they contained what I would call "academic" treatments of neural networks. That means they covered all kinds of varieties of neural networks, including brain-state-in-a-box, Boltzmann machines, Hebbian networks, Adaline, ART1, ART2, and many more. Now I have nothing against learning about these techniques on the graduate school level, or even on the undergraduate level. But for practitioners, I see absolutely no advantage here because they aren't used in practice. Nearly always, when someone says they are building a "neural network" they mean a Multi-layered perceptron (MLP).
When I use neural networks in major software packages, such as IBM-SPSS Modeler, Statistica, Tibco Spotfire Miner, SAS Enterprise Miner, JMP, Affinium Predictive Insight, and I can go on... I am building MLPs, not ART3 models. So why teach professionals how these other algorithms work? I don't know.
Now neural network experts I'm sure will find times and places to build esoteric varieties of neural nets. But because of the way most practitioners actually build neural networks, I recommend sticking with the MLP, and understanding the vast numbers of options one has just with this algorithm. This is one reason I like the Christopher Bishop Neural Networks for Pattern Recognition. Check out the table of contents--I think these topics are more helpful to understand than learning more neural network algorithms.
Another option for spinning up on neural nets is the excellent SAS Neural Network FAQ which is old, but still a very clear introduction to the subject. Finally, for backpropagation, I also like the Richard Lippmann 1987 classic "An Introduction to Computing with Neural Nets (8MB here).