The Connectivity Analysis of Simple Association

Part of Neural Information Processing Systems 0 (NIPS 1987)

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Dan Hammerstrom


The efficient realization, using current silicon technology, of Very Large Connection Networks (VLCN) with more than a billion connections requires that these networks exhibit a high degree of communication locality. Real neural networks exhibit significant locality, yet most connectionist/neural network models have little. In this paper, the connectivity requirements of a simple associative network are analyzed using communication theory. Several techniques based on communication theory are presented that improve the robust(cid:173) ness of the network in the face of sparse, local interconnect structures. Also discussed are some potential problems when information is distributed too widely.