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Analysis of the two-dimensional receptive fields learned by the Generalized Hebbian Algorithm in response to random input
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Quadrature and the development of orientation selective cortical cells by Hebb rules
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Simplified neuron model as a principal component analyzer
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Spontaneous symmetry-breaking energy functionsand the emergence of orientation selective cortical cells
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Spontaneous symmetry-breaking energy functions and the emergence of orientation selective cortical cells
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Neural theory of association and concept-formation
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Connectionist learning procedures
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The visual cortex as a spatial frequency analyser
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Two-dimensional spectral analysis of cortical receptive field profiles
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Spatial visual channels in the fourier plane
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Optimal constrained representation and filtering of signals
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Development of feature-analyzing cells and their columnar organization in a layered self-adaptive network
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Exploration of the primary visual cortex, 1955–78
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Self-organization in a perceptual network
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Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression
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Six formal properties of two-dimensional anisotropie visual filters: Structural principles and frequency/orientation selectivity
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Visual cortical neurons as localized spatial frequency filters
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Relationship between spatial frequency selectivity and receptive field profile of simple cells.
inferredcites →https://doi.org/10.1113/jphysiol.1985.sp015776openalex-snapshot
Spatial and temporal frequency selectivity of neurones in visual cortical areas V1 and V2 of the macaque monkey.
inferredcites →https://doi.org/10.1126/science.7233231openalex-snapshot
Phase Relationships Between Adjacent Simple Cells in the Visual Cortex
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Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A General Class of Neural Networks for Principal Component Analysis and Factor Analysis
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Seeking Independence Using Biologically-Inspired ANN’s
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PCA Properties of Interneurons
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Non-linear data structure extraction using simple hebbian networks
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Visual channels, Hebbian assemblies and the effect of Hebb's rule
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Noise-robust acoustic signature recognition using nonlinear Hebbian learning
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ε-insensitive Hebbian learning
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Unsupervised neural networks for the identification of minimum overcomplete basis in visual data
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Karhunen-Loeve transform using neural networks
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Unsupervised texture segmentation by Hebbian learnt cortical cells
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Nonlinear Hebbian Learning for noise-independent vehicle sound recognition
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Perimeter Security on Detecting Acoustic Signature of Approaching Vehicle Using Nonlinear Neural Computation
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Introducing Asymmetry into Interneuron Learning
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Psychophysically Tuned Divisive Normalization Approximately Factorizes the PDF of Natural Images
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Simulation of white-light adaptation characteristics with use of nonlinear neural principal component analysis
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Acquisition of Information Representation by Neural Networds
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Linear Subspaces of the Appearance Space
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Extraction of Inherent Frequency Components of Multiway EEG Data Using Two-Stage Neural Canonical Correlation Analysis
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Adaptive Transform Coding of Images Using a Mixture of Principal Components
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