Everything about Generalized Hebbian algorithm
Linear feedforward neural network model
The generalized Hebbian algorithm, also known in the literature as Sanger's rule, is a linear feedforward neural network for unsupervised learning with applications primarily in principal components analysis. First defined in 1989, it is similar to Oja's rule in its formulation and stability, except it can be applied to networks with multiple outputs. The name originates because of the similarity between the algorithm and a hypothesis made by Donald Hebb about the way in which synaptic strengths in the brain are modified in response to experience, i.e., that changes are proportional to the correlation between the firing of pre- and post-synaptic neurons. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Generalized Hebbian algorithmA 5-minute orientation
- Pick up a classic book: Neuro-fuzzy and soft computingThe most-reprinted book on the subject
- Read the landmark paper: Analysis of the two-dimensional receptive fields learned by the Generalized Hebbian Algorithm in response to random inputCited 26 times
- Go sideways: Oja's ruleA closely related topic
- Go sideways: Synaptic weightA closely related topic
📖 Definitions
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🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Analysis of the two-dimensional receptive fields learned by the Generalized Hebbian Algorithm in response to random input
An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm
Efficient hardware architecture based on generalized Hebbian algorithm for texture classification
Parallel Generalized Hebbian Algorithm for Large Scale Data Analytics
Fuzzy Generalized Hebbian Algorithm for Large-Scale Intrusion Detection System
FPGA Implementation of Generalized Hebbian Algorithm for Texture Classification
An effective classification approach for big data with parallel generalized Hebbian algorithm
High-Level Synthesis-Based FPGA Hardware Accelerator for Generalized Hebbian Learning Algorithm for Neuromorphic Computing
📚 Books worth reading
Neuro-fuzzy and soft computingJyh-Shing Roger Jang, Chuen-Tsai Sun · 1996
Neural networksSimon S. Haykin · 1994
An introduction to the modeling of neural networksPierre Peretto · 1992
Neural networksBerndt Müller, Joachim Reinhardt · 1995
Introduction to artificial neural systemsJacek M. Zurada · 1992
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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- Unsupervised learning Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled…