On-line learning in neural networks
On-line learning in neural networks
Learning Synaptic Clusters for Nonlinear Dendritic Processing
Neural Processing Letters
Probability Density Estimation Using Adaptive Activation Function Neurons
Neural Processing Letters
Entropy Optimization - Application to Blind Source Separation
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Notes on Bell-Sejnowski PDF-matching neuron
Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
An asymptotic property of model selection criteria
IEEE Transactions on Information Theory
Multidimensional density shaping by sigmoids
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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In recent work, we introduced nonlinear adaptive activation function (FAN) artificial neuron models, which learn their activation functions in an unsupervised way by information-theoretic adapting rules. We also applied networks of these neurons to some blind signal processing problems, such as independent component analysis and blind deconvolution. The aim of this letter is to study some fundamental aspects of FAN units' learning by investigating the properties of the associated learning differential equation systems.