Simple neuron models for independent component analysis
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Updated: Oct 21, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
1Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland. aapo.hyvarinen@hut.fihttp://nucleus.hut.fi/-aapo
Abstract:
Recently, several neural algorithms have been introduced for Independent component Analysis. Here we approach the problem from the point of view of a single neuron. First, simple Hebbian-like learning rules are introduced for estimating one of the independent components from sphered data. Some of the learning rules can be used to estimate an independent components which has a negative kurtosis, and the others estimate a component of positive kurtosis. Next, a two-unit system is introduced to estimate an independent component of any kurtosis. The results are then generalized to estimate independent components from non-sphered (raw) mixtures. To separate several independent components, a system of several neurons with linear negative feedback is used. The convergence of the learning rules is rigorously proven without any unnecessary hypotheses on the distribution of the independent components.
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