Correlations in high dimensional or asymmetric data sets: Hebbian neuronal processing

The Hebbian neural learning algorithm that implements Principal Component Analysis (PCA) can be extended for the analysis of more realistic forms of neural data by including higher than two-channel correlations and non-Euclidean 1 p metrics. Maximizing a dth rank tensor form which correlates d chann...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Neural networks 1991, Vol.4 (3), p.337-347
Hauptverfasser:	Softky, William R., Kammen, Daniel M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied sciences Asymmetric coding Correlation functions Exact sciences and technology Hebbian learning Information theory Information, signal and communications theory Multidimensional analysis Non-Euclidean metrics Principal component analysis Self-organization Telecommunications and information theory
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!