Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Self-Organizing Maps
A Model of Border-Ownership Coding in Early Vision
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Surrounding Suppression and Facilitation in the Determination of Border Ownership
Journal of Cognitive Neuroscience
Intermediate-level visual representations and the construction of surface perception
Journal of Cognitive Neuroscience
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A computational model of motor areas based on bayesian networks and most probable explanations
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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Mammalian visual cortex is known to have various neuronal response properties that depend on stimuli outside classical receptive fields. In this article, we give a probabilistic explanation to one such property called border-ownership signals, by interpreting them as posterior joint probabilities of a low-level edge property and a high-level figure property. We show that such joint probabilities can be found in a hierarchical Bayesian network mimicking visual cortex, and indeed they exhibit simulational responses qualitatively similar to physiological data.