Distributed knowledge representation in fully connected networks

Author

Gattiker, J.R.

Author_Institution

Los Alamos Nat. Lab., NM, USA

fYear

1996

fDate

4-5 Nov 1996

Firstpage

84

Lastpage

88

Abstract

Fully-connected binary networks, in addition to implementing content addressable memories, have been shown to be capable of encoding arbitrary limit cycles using synchronous dynamics. A stochastic knowledge representation paradigm is proposed, and a way to encode this knowledge form into cycles in fully-connected networks is described. This new representation format stores information in a truly distributed manner across the network, as opposed to previous schemes which store one knowledge atom per neuron

Keywords

content-addressable storage; encoding; graph theory; knowledge representation; limit cycles; perceptrons; stochastic processes; content addressable memories; distributed knowledge representation; encoding; fully-connected binary networks; graph knowledge; information storage; limit cycles; perceptron networks; stochastic knowledge representation; synchronous dynamics; Artificial intelligence; Associative memory; Biological information theory; Encoding; Intelligent networks; Knowledge representation; Laboratories; Limit-cycles; Pattern analysis; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligence and Systems, 1996., IEEE International Joint Symposia on

Conference_Location

Rockville, MD

Print_ISBN

0-8186-7728-7

Type

conf

DOI

10.1109/IJSIS.1996.565055

Filename

565055