A Neural Network Model for Concept Formation

Acquisition of abstract concept is the key step in human intelligence development, but the neural mechanism of concept formation is not clear yet. Researches on complexity and self organization theory indicate that concept is a result of emergence of neural system and it should be represented by an attractor. Associative learning and hypothesis elimination are considered as the mechanisms of concept formation, and we think that Hebbian learning rule can be used to describe the two operations. In this paper, a neural network is constructed based on Hopfield model, and the weights are updated according to Hebbian rule. The forming processes of natural concept, number concept and addition concept are simulated by using our model. Facts from real neuroanatomy provide some evidences for our model.