Unsupervised Bayesian Network Learning for Object Recognition in Image Sequences
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Implementing a hierarchical Bayesian visual cortex model on multi-core processors
Proceedings of the 47th Annual Southeast Regional Conference
On the prospects for building a working model of the visual cortex
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Parallelizing two classes of neuromorphic models on the cell multicore architecture
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Optimizing hierarchical temporal memory for multivariable time series
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
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We address the technical challenges involved in combining key features from several theories of the visual cortex in a single coherent model. The resulting model is a hierarchical Bayesian network factored into modular component networks embedding variable-order Markov models. Each component network has an associated receptive field corresponding to components residing in the level directly below it in the hierarchy. The variable-order Markov models account for features that are invariant to naturally occurring transformations in their inputs. These invariant features give rise to increasingly stable, persistent representations as we ascend the hierarchy. The receptive fields of proximate components on the same level overlap to restore selectivity that might otherwise be lost to invariance.