1995
DOI: 10.1007/bf02279930
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Evolving neural networks with iterative learning scheme for associative memory

Abstract: A locally iterative learning (LIL) rule is adapted to a model of the associative memory based on the evolving recurrent-type neural networks composed of growing neurons. There exist extremely di erent scale parameters of time, the individual learning time and the generation in evolution. This model allows us de nite investigation on the interaction between learning and evolution. And the reinforcement of the robustness against the noise is also achieved in the evolutional scheme.

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Cited by 2 publications
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“…Another approach has been to synthesize polymerizable analogs of beta-galactosyl ceramide. Such compounds have specific affinity for recombinant gp120 and may have potential anti-viral effects, inhibiting infection or transcytosis of HIV by epithelial cells lining the gastrointestinal, anorectal or genitourinary tracts [81].…”
Section: Antiviral Implicationsmentioning
confidence: 99%
“…Another approach has been to synthesize polymerizable analogs of beta-galactosyl ceramide. Such compounds have specific affinity for recombinant gp120 and may have potential anti-viral effects, inhibiting infection or transcytosis of HIV by epithelial cells lining the gastrointestinal, anorectal or genitourinary tracts [81].…”
Section: Antiviral Implicationsmentioning
confidence: 99%