ArticleScientific reports2018
Error-Gated Hebbian Rule: A Local Learning Rule for Principal and Independent Component Analysis.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- A biological model of nonlinear dimensionality reduction.Science advances · 2025Article
- Adaptive control of synaptic plasticity integrates micro- and macroscopic network function.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Review
- The neural coding framework for learning generative models.Nature communications · 2022Article
- Feature alignment as a generative process.Frontiers in artificial intelligence · 2022Article
- Sub-Optimality of the Early Visual System Explained Through Biologically Plausible Plasticity.Frontiers in neuroscience · 2021Article
- 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
- A Measure of Information Available for Inference.Entropy (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed a biologically plausible unsupervised learning algorithm, error-gated Hebbian rule (EGHR)-β, that performs principal component analysis (PCA) and independent component analysis (ICA) in a single-layer feedforward neural network. If parameter β = 1, it can extract the subspace that major principal components span similarly to Oja's subspace rule for PCA. If β = 0, it can separate independent sources similarly to Bell-Sejnowski's ICA rule but without requiring the same number of input and output neurons. Unlike these engineering rules, the EGHR-β can be easily implemented in a biological or neuromorphic circuit because it only uses local information available at each synapse. We analytically and numerically demonstrate the reliability of the EGHR-β in extracting and separating major sources given high-dimensional input. By adjusting β, the EGHR-β can extract sources that are missed by the conventional engineering approach that first applies PCA and then ICA. Namely, the proposed rule can successfully extract hidden natural images even in the presence of dominant or non-Gaussian noise components. The results highlight the reliability and utility of the EGHR-β for large-scale parallel computation of PCA and ICA and its future implementation in a neuromorphic hardware.
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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.