An attractor neural network model of semantic fact retrieval

This paper presents an attractor neural network model of semantic fact retrieval , based on Collins and Quillian's original semantic network models. In the context of modeling a semantic network, a distinction is made between associations linking together objects belonging to hierarchically-related semantic classes, and associations linking together objects and their attributes. Using a distributed representation leads to some generalization properties that have computational advantage. Simulations performed demonstrate that it is feasible to get reasonable response performance regarding various semantic queries, and that the temporal pattern of retrieval times obtained in simulations is consistent with psychological experimental data. Therefore, it is shown that attractor neural networks can be successfully used to model higher level cogni-tive phenomena than standard content addressable pattern recognition.