[eythian]

As someone who has/does dabble in genetic algorithms and neural networks, it's always wise to keep in the back of ones mind that these systems are inspired by biology, and not generally an attempt to replicate it[0]. It's also often useful to go back to biology with an eye for ideas to steal, but rarely are they are useful model to inform biology or biological understanding.

As an anecdote, I once had a summer project between the neuroscience and computer science departments at my university. They had data from rat brains that they'd potentiated parts of (basically, zapped some neurons so they connection weights (in NN terms) got messed up and were sending too-strong signals to their neighbours), and how that potentiation decayed over time. They got me to attempt to reproduce it in a neural network. So, I build an NN system with the ability to have neurons zapped and managed to reproduce their results. But NNs being a very abstract model of a set of neurons, there are a lot of parameters that can be twiddled. By making fairly small changes to those parameters, I managed to get the exact inverse of their results also.

[0] this applies both to computer scientists building them, and also to biologists looking at them and going "that's a really poor attempt to be a brain, look at all the things it's missing."