Minimally Connective, Auto-associative, Neural Networks
作者:
DAVID VOGEL,
WILLIAM BOOS,
期刊:
Connection Science
(Taylor Available online 1994)
卷期:
Volume 6,
issue 4
页码: 461-469
ISSN:0954-0091
年代: 1994
DOI:10.1080/09540099408915734
出版商: Taylor & Francis Group
关键词: Neural;projective;block design;strongly regular;network;P-net;associative;connectionist;connective;sparse code;memory.
数据来源: Taylor
摘要:
Classic barriers to using auto-associative neural networks to model mammalian memory include the unrealistically high synaptic connectivity of fully connected networks, and the relative paucity of information that has been stored in networks with realistic numbers of synapses per neuron and learning rules amenable to physiological implementation. We describe extremely large, auto-associative networks with low synaptic density. The networks have no direct connections between neurons of the same layer. Rather, the neurons of one layer are 'linked' by connections to neurons of some other layer. Patterns of projections of one layer on to another which form projective planes, or other cognate geometries, confer considerable computational power an the network.
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