The thing about neural networks is that they don't really behave much like the brain at all. It's a thin metaphor that managed to catch on and now whenever I say I'm training a neural network people assume that I'm into some real mystical shit. There's a sort-of comparison there in that the "neurons" of a neural network are nodes where input signals are mapped to output signals, but it's mostly a superficial likeness. It could be worse—at least it's not the "god algorithm" or anything.
Engineers at the University of Michigan are onto something rather more brainlike, however, with help from a peculiar electrical component known as a memristor. They've developed a new "sparse coding" algorithm that uses grids of memristors to approximate the pattern recognition abilities of mammalian brains. The result, which is described in the current Nature Nanotechnology, is potentially much faster image processing—or processing of any other very large datasets that currently require a lot of computing resources to deal with.
The hardware prototype described in the paper consists a 32 by 32 array of memristors. A memristor is basically a normal resistor (an electrical component that limits current) with a memory of sorts. Its resistance, or the amount of current it blocks, changes based on the voltages that have been applied to it in the past. (A normal resistor is going to stay about the same regardless of this voltage history.)