Introduction to the theory of neural computation
Introduction to the theory of neural computation
Learning invariance from transformation sequences
Neural Computation
Neural networks for pattern recognition
Neural networks for pattern recognition
A multiple cause mixture model for unsupervised learning
Neural Computation
Self-organizing maps
A Neural Network for PCA and Beyond
Neural Processing Letters
A simple algorithm that discovers efficient perceptual codes
Computational and psychophysical mechanisms of visual coding
Toward a biophysically plausible bidirectional Hebbian rule
Neural Computation
The handbook of brain theory and neural networks
Feature extraction through LOCOCODE
Neural Computation
Neural Computation and Self-Organizing Maps; An Introduction
Neural Computation and Self-Organizing Maps; An Introduction
Competition and multiple cause models
Neural Computation
Recurrent network with large representational capacity
Neural Computation
Learning Viewpoint Invariant Perceptual Representations from Cluttered Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchial self-organization of minicolumnar receptive fields
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Nonlinear Complex-Valued Extensions of Hebbian Learning: An Essay
Neural Computation
A Feedback Model of Visual Attention
Journal of Cognitive Neuroscience
A Generalized Divergence Measure for Nonnegative Matrix Factorization
Neural Computation
Learning Image Components for Object Recognition
The Journal of Machine Learning Research
Selectivity and Stability via Dendritic Nonlinearity
Neural Computation
Maximal Causes for Non-linear Component Extraction
The Journal of Machine Learning Research
Unsupervised learning of overlapping image components using divisive input modulation
Computational Intelligence and Neuroscience
Generalized softmax networks for non-linear component extraction
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A low-order model of biological neural networks
Neural Computation
Adaptive feedback inhibition improves pattern discrimination learning
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Attention improves the recognition reliability of backpropagation network
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Neural coding strategies and mechanisms of competition
Cognitive Systems Research
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A large and influential class of neural network architectures uses postintegration lateral inhibition as a mechanism for competition. We argue that these algorithms are computationally deficient in that they fail to generate, or learn, appropriate perceptual representations under certain circumstances. An alternative neural network architecture is presented here in which nodes compete for the right to receive inputs rather than for the right to generate outputs. This form of competition, implemented through preintegration lateral inhibition, does provide appropriate coding properties and can be used to learn such representations efficiently. Furthermore, this architecture is consistent with both neuroanatomical and neurophysiological data. We thus argue that preintegration lateral inhibition has computational advantages over conventional neural network architectures while remaining equally biologically plausible.