What does the retina know about natural scenes?
Neural Computation
A neural model of contour integration in the primary visual cortex
Neural Computation
Learning to see: genetic and environmental influences on visual development
Learning to see: genetic and environmental influences on visual development
The world from a cat’s perspective – statistics of natural videos
Biological Cybernetics
Neural Computation
Emergence of Topographic Cortical Maps in a Parameterless Local Competition Network
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
The parameterless self-organizing map algorithm
IEEE Transactions on Neural Networks
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The goal of this paper is to extend the ecological theory of perception to the larger scale spatial organization of cortical maps. This leads to the hypothesis that cortical organization of responses to visual features reflects the environmental organization of these same features. In our previous work we have shown that the spatial statistics of natural images can be characterized by a slowly decaying, or low frequency correlational structure for color, and a rapidly decaying, or high-frequency structure for orientation features. A similar contrasting behavior of spatial statistics for color and orientation was measured in parallel in the cortical response of macaque visual cortex. In order to explore whether this parallel is meaningful, we performed a cortical simulation using an adaptation of Kohonen's self-organizing map algorithm. The simulated cortex responds to both low-frequency and high-frequency input visual features, and learns to represent these features through weight modification. We demonstrate that the learnt cortical weights show the same spatial correlation structure that is observed both in natural image statistics and the measured cortical responses.