Association Field

Sparse Coding Of Natural Images Using A Prior On Edge Co-Occurences
Oriented edges in images of natural scenes tend to be aligned in co-linear or co-circular arrangements, with lines and smooth curves …
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Edge co-occurrences can account for rapid categorization of natural versus animal images
Making a judgment about the semantic category of a visual scene, such as whether it contains an animal, is typically assumed to involve …
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Edge co-occurrences can account for rapid categorization of natural versus animal images
Edge co-occurrences are sufficient to categorize natural versus animal images
Analysis and interpretation of a visual scene to extract its category, such as whether it contains an animal, is typically assumed to …
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Phase space analysis of networks based on biologically realistic parameters
We study cortical network dynamics for a spatially embedded network model. It represents, in terms of spatial scale, a large piece of …
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Role of homeostasis in learning sparse representations
Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the …
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Role of homeostasis in learning sparse representations
Adaptive Sparse Spike Coding : applications of Neuroscience to the compression of natural images
If modern computers are sometimes superior to cognition in some specialized tasks such as playing chess or browsing a large database, …
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Adaptive Sparse Spike Coding : applications of Neuroscience to the compression of natural images
Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas
Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas
Coding static natural images using spiking event times: do neurons cooperate?
To understand possible strategies of temporal spike coding in the central nervous system, we study functional neuromimetic models of …
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Coding static natural images using spiking event times: do neurons cooperate?
Sparse spike coding in an asynchronous feed-forward multi-layer neural network using matching pursuit
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