Neuromorphic computing and the brain that wouldn't die ZDNet

Inspired by a theory into the organisms of memory and recall in the brain, neural networking is a digital simulation of how synapses may retain information, after being trained to recognize patterns. For instance, neural nets enable a computer, or perhaps a cloud-based service, to recognize the characters of printed text without the need for programming explicitly specifying what text is, or how it can spot a certain face in a crowd after having seen several photographs of the same face. As a neural networking problem becomes linearly broader -- for example, distinguishing one form of written text from another -- the data required to train it grows exponentially larger. There's a valid argument that some of the tasks being envisioned for neural nets, such as spotting when anyone is getting depressed or agitated, may be impossible, even with today's storage and memory technologies. So the revelations by researchers that chemical structures comprised of completely random assemblies of nanometer-scale wires may exhibit the electrical characteristics of memory in a brain perhaps shouldn't continue to be dismissed for much longer.