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TNN
2010
121views Management» more  TNN 2010»
13 years 4 months ago
Foundations of implementing the competitive layer model by Lotka-Volterra recurrent neural networks
The competitive layer model (CLM) can be described by an optimization problem. The problem can be further formulated by an energy function, called the CLM energy function, in the s...
Zhang Yi
GECCO
2007
Springer
182views Optimization» more  GECCO 2007»
14 years 3 months ago
Generating large-scale neural networks through discovering geometric regularities
Connectivity patterns in biological brains exhibit many repeating motifs. This repetition mirrors inherent geometric regularities in the physical world. For example, stimuli that ...
Jason Gauci, Kenneth O. Stanley
GECCO
1999
Springer
130views Optimization» more  GECCO 1999»
14 years 1 months ago
Heterochrony and Adaptation in Developing Neural Networks
This paper discusses the simulation results of a model of biological development for neural networks based on a regulatory genome. The model’s results are analyzed using the fra...
Angelo Cangelosi
ICANN
1997
Springer
14 years 1 months ago
Correlation Coding in Stochastic Neural Networks
Abstract. Stimulus4ependent changes have been observed in the correlations between the spike trains of simultaneously-recorded pairs of neurons from the auditory cortex of marmoset...
Raphael Ritz, Terrence J. Sejnowski
TNN
1998
146views more  TNN 1998»
13 years 9 months ago
Fuzzy lattice neural network (FLNN): a hybrid model for learning
— This paper proposes two hierarchical schemes for learning, one for clustering and the other for classification problems. Both schemes can be implemented on a fuzzy lattice neu...
Vassilios Petridis, Vassilis G. Kaburlasos