Although, as Doug pointed out in class, there are important differences between connectionist networks on computers and neurons in the brain, I still think that the name "Neural Networks" is justified by the fundamental similarity between the two.
This fundamental similarity, which I think is very important and which also implies many promising possibilities for simulated neural nets--based on phenomena in the brain--that have not yet been explored in AI research, ...this fundamental similarity is the way in which information is manipulated through destroying some info and then copying the result and distributing it to create new info. That is, when the activations of input nodes in a computer network are summed in a node, which node each of those signals came from is lost. But the information that remains--the combined strength of those inputs--is then copied and distributed to nodes in the next layer, where the process is repeated. I think that this method of information manipulation is an incredibly important concept in and of itself; therefore, we should acknowledge connectionist networks' debt to the structure of the brain. Furthermore, I think there is (or will eventually be) a lot more we can do with neural nets based on observations of how the brain works.
