ArticleScientific reports2016
A Local Learning Rule for Independent Component Analysis.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Estimating the motor exploration in reinforcement learning.iScience · 2026Article
- A general framework for interpretable neural learning based on local information-theoretic goal functions.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Adaptive control of synaptic plasticity integrates micro- and macroscopic network function.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Review
- Data mining analyses for precision medicine in acromegaly: a proof of concept.Scientific reports · 2022Article
- Feature alignment as a generative process.Frontiers in artificial intelligence · 2022Article
- Learning to represent signals spike by spike.PLoS computational biology · 2020Article
- Multi-context blind source separation by error-gated Hebbian rule.Scientific reports · 2019Article
- On the Sparse Structure of Natural Sounds and Natural Images: Similarities, Differences, and Implications for Neural Coding.Frontiers in computational neuroscience · 2019Article
- Principles underlying the input-dependent formation and organization of memories.Network neuroscience (Cambridge, Mass.) · 2019Article
- In vitro neural networks minimise variational free energy.Scientific reports · 2018Article
- Theoretical Models of Neural Development.iScience · 2018Review
- Review
- A Measure of Information Available for Inference.Entropy (Basel, Switzerland) · 2018Article
- Error-Gated Hebbian Rule: A Local Learning Rule for Principal and Independent Component Analysis.Scientific reports · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Humans can separately recognize independent sources when they sense their superposition. This decomposition is mathematically formulated as independent component analysis (ICA). While a few biologically plausible learning rules, so-called local learning rules, have been proposed to achieve ICA, their performance varies depending on the parameters characterizing the mixed signals. Here, we propose a new learning rule that is both easy to implement and reliable. Both mathematical and numerical analyses confirm that the proposed rule outperforms other local learning rules over a wide range of parameters. Notably, unlike other rules, the proposed rule can separate independent sources without any preprocessing, even if the number of sources is unknown. The successful performance of the proposed rule is then demonstrated using natural images and movies. We discuss the implications of this finding for our understanding of neuronal information processing and its promising applications to neuromorphic engineering.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.