An application of the principle of maximum information preservation to linear systems
Advances in neural information processing systems 1
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Multiscale Feature Extraction from the Visual Environment in an Active Vision System
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Biologically Inspired Architecture of Feedforward Networks for Signal Classification
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Reduced representation by neural networks with restricted receptive fields
Neural Computation
Bayesian self-organization driven by prior probability distributions
Neural Computation
Neural Computation
Neural Computation
Adaptive sensory processing for efficient place coding
Neurocomputing
Using Human Visual System modeling for bio-inspired low level image processing
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Local non-linear interactions in the visual cortex may reflect global decorrelation
Journal of Computational Neuroscience
Information theory in neuroscience
Journal of Computational Neuroscience
Automatic inference of cabinet approval ratings by information-theoretic competitive learning
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Transmission of population-coded information
Neural Computation
Task-Oriented sparse coding model for pattern classification
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
A model for the receptive field of retinal ganglion cells
Neural Networks
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We propose a theory of the early processing in the mammalian visual pathway. The theory is formulated in the language of information theory and hypothesizes that the goal of this processing is to recode in order to reduce a generalized redundancy subject to a constraint that specifies the amount of average information preserved. In the limit of no noise, this theory becomes equivalent to Barlow's redundancy reduction hypothesis, but it leads to very different computational strategies when noise is present. A tractable approach for finding the optimal encoding is to solve the problem in successive stages where at each stage the optimization is performed within a restricted class of transfer functions. We explicitly find the solution for the class of encodings to which the parvocellular retinal processing belongs, namely linear and nondivergent transformations. The solution shows agreement with the experimentally observed transfer functions at all levels of signal to noise.