I just finished reading Complexity: A Guided Tour by Melanie Mitchell. The book is meant to be an introduction to complexity theory for the general reader.
The book works as a lucid review of many interesting topics in science and mathematics. I'd read Mitchell's book on genetic algorithms, and she's a gifted writer. Here she explores (among other things) dynamical systems, chaos, information theory, genetic algorithms, cellular automata, analogical reasoning, and network theory. She does a great job explaining difficult concepts in a clear manner.
"You think you know when you learn, are more sure when you can write, even more when you can teach, but certain when you can program." 
All this Neurdon hullabaloo over memristors and Kurzweilian futurism has got me thinking about the inevitable media question concerning all this: Will our RoboSlave Bots learn to love us in a somewhat creepy, Haley Joel Osment 
I've thought a bit about how modelers approach brain areas whose functions are still not very well constrained by robust neurophysiological data. By this, I mean that there is simply not enough data to say, in plain terms, what that particular brain area does. In terms of visual cortex, this pretty much accounts for all areas beyond V1, namely V2, V3, V4, posterior IT (ITp), anterior IT (ITa), which all form a loose hierarchy (in the order they're listed), and whatever areas of the temporal lobe may be 'visual', e.g. entorhinal. These words may sound a bit harsh, or even better, like flame-bait. Yet, when a major computationalist publishes an article titled 
A recent article on the WSJ (
Justin Mullins is the author of a nice post on the Memristor, appeard on 7/8/2009 on