A major problem in artificial brain building is the automatic construction and training of multi-module systems of neural networks. For example, consider a biological human brain, which has millions of neural nets. If an artificial brain is to have similar complexity, it is unrealistic to require that the training data set for each neural net must be specified explicitly by a human, or that interconnections between evolved nets be performed manually. In this paper we present an original technique to solve this problem. A single large-scale task (too complex to be performed by a single neural net) is automatically split into simpler sub-tasks. A multi-module system of neural nets is then trained so that one of these sub-tasks is performed by each net. We present the results of an experiment using this novel technique for pattern recognition.
['Jonathan Dinerstein', 'N. Dinerstein', 'H. de Garis']
Automatic multi-module neural network evolution in an artificial brain