Artificial Neural Networks
Description:
Artificial Neural Networks (ANNs) are information processing models that are inspired by the way biological nervous systems, such as the brain, process information. The models are composed of a large number of highly interconnected processing elements (neurones) working together to solve specific problems. ANNs, like people, learn by example. Contrary to conventional computers, that can only solve problems if the set of instructions or algorithms are known, ANNs are very flexible, powerfull and trainable. Conventional computers and neural networks are complementary: a large number of tasks require the combination of a learning approach and a set of instructions. Mostly, the conventional computer is used to supervise the neural network.
Enablers:
1.
Inhibitors:
Paradigms:
Experts:
Timing:
1943: the first artificial neuron was produced by the neurophysiologist Warren McCulloch and the logician Walter Pits.
1986: David Rumelhart & James McClelland train a network of 920 artificial neurons to form the past tenses of English verbs (University of California at San Diego).
Web Resources:
1. http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html