Independent component analysis by general nonlinear Hebbian-like learning rules
From MaRDI portal
Publication:1129228
DOI10.1016/S0165-1684(97)00197-7zbMath0893.94034WikidataQ127281744 ScholiaQ127281744MaRDI QIDQ1129228
Publication date: 13 August 1998
Published in: Signal Processing (Search for Journal in Brave)
Neural networksHigher-order statisticsRobustnessHebbian learningIndependent component analysisBlind source separation
Learning and adaptive systems in artificial intelligence (68T05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)
Related Items (25)
Cross-Talk Induces Bifurcations in Nonlinear Models of Synaptic Plasticity ⋮ Efficient optimization of reference-based negentropy for noncircular sources in complex ICA ⋮ Using independent component for clustering of time series data ⋮ Hebbian crosstalk and input segregation ⋮ An efficient semi-blind source extraction algorithm and its applications to biomedical signal extraction ⋮ Directed acyclic graph based information shares for price discovery ⋮ A Taxonomy for Spatiotemporal Connectionist Networks Revisited: The Unsupervised Case ⋮ On the distance concentration awareness of certain data reduction techniques ⋮ ECOPICA: empirical copula-based independent component analysis ⋮ Independent component analysis based on fast proximal gradient ⋮ Blind Nonnegative Source Separation Using Biological Neural Networks ⋮ Complexity Pursuit: Separating Interesting Components from Time Series ⋮ Eigenvectors of Orthogonally Decomposable Functions ⋮ A Unifying Framework of Synaptic and Intrinsic Plasticity in Neural Populations ⋮ A Theory for Learning by Weight Flow on Stiefel-Grassman Manifold ⋮ Stochastic Trapping in a Solvable Model of On-Line Independent Component Analysis ⋮ Data-guided model combination by decomposition and aggregation ⋮ Fault diagnosis of non-Gaussian process based on FKICA ⋮ Spiking Neurons Can Learn to Solve Information Bottleneck Problems and Extract Independent Components ⋮ A novel method for complex-valued signals in independent component analysis framework ⋮ A minimal model of the interaction of social and individual learning ⋮ Optimization and testing in linear non‐Gaussian component analysis ⋮ Blind source separation with nonlinear autocorrelation and non-Gaussianity ⋮ Linear Non-Gaussian Component Analysis Via Maximum Likelihood ⋮ Synergies Between Intrinsic and Synaptic Plasticity Mechanisms
This page was built for publication: Independent component analysis by general nonlinear Hebbian-like learning rules