NDLI: Training and holistic computation of vector graphics with Hebbian bases in contrast to RAAM networks

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Synaptic modification of interneuron afferents in a hippocampal CA3 model prevents activity oscillations

Defining time in a minimal hippocampal CA3 model by matching time-span of associative synaptic modification and input pattern duration

Timing of consecutive traveling pulses in a model of entorhinal cortex

Latent attractor selection for variable length episodic context stimuli with distractors

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T-maze training of a recurrent CA3 model reveals the necessity of novelty-based modulation of LTP in hippocampal region CA3

The importance of stop word removal on recall values in text categorization

Training and holistic computation of vector graphics with Hebbian bases in contrast to RAAM networks

Fault detection system in gas lift well based on artificial immune system

An investigation into the source of power for AIRS, an artificial immune classification system

Self-organizing neural networks for efficient clustering of gene expression data

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Probabilistic neural networks for multi-class tissue discrimination with gene expression data

Genetic search for optimal ensemble of feature-classifier pairs in DNA gene expression profiles

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Integrated gene expression analysis of multiple microarray data sets based on a normalization technique and on adaptive connectionist model

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Multivariate time series model discovery with similarity-based neuro-fuzzy networks and genetic algorithms

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Training and holistic computation of vector graphics with Hebbian bases in contrast to RAAM networks

Content Provider	IEEE Xplore Digital Library
Author	Schaefer, M. Dilger, W.
Copyright Year	2003
Description	Author affiliation: Chemnitz Univ. of Technol., Germany (Schaefer, M.; Dilger, W.)
Abstract	Hebbian Learning is well-known for training of associative networks whereas recursive auto-associative memory (RAAM) learning uses auto-associative networks which are trained to represent structured information like parse trees of natural sentences or logical terms. In this paper Hebbian learning is used for representing structured information in terms of vector graphic. The resulting networks are holistically computed. Furthermore, a theorem relating bipolar Hebbian learning is proved.
Sponsorship	Int. Neural Network Soc. IEEE Neural Networks Soc
Starting Page	1667
Ending Page	1672
File Size	428555
Page Count	6
File Format	PDF
ISBN	0780378989
ISSN	10987576
DOI	10.1109/IJCNN.2003.1223657
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer networks Graphics Intelligent networks Hebbian theory Neurons Decoding Chemical technology Tree graphs Natural languages Concrete
Content Type	Text
Resource Type	Article

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