On evidential reasoning in a hierarchy of hypotheses
Artificial Intelligence
Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Neural Computation
Learning in graphical models
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Training products of experts by minimizing contrastive divergence
Neural Computation
Fields of Experts: A Framework for Learning Image Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
PAMPAS: real-valued graphical models for computer vision
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A preliminary investigation of a neocortex model implementation on the Cray XD1
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
ACMOS'08 Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation
Unsupervised Bayesian Network Learning for Object Recognition in Image Sequences
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Scaling analysis of a neocortex inspired cognitive model on the Cray XD1
The Journal of Supercomputing
Character Recognition Using Hierarchical Vector Quantization and Temporal Pooling
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
A context switching streaming memory architecture to accelerate a neocortex model
Microprocessors & Microsystems
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
Resource constraints on computation and communication in the brain
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Parallelizing two classes of neuromorphic models on the cell multicore architecture
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A neural network model for a hierarchical spatio-temporal memory
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
A neuromorphic approach to computer vision
Communications of the ACM
Research frontier: deep machine learning--a new frontier in artificial intelligence research
IEEE Computational Intelligence Magazine
Reverse-engineering the human auditory pathway
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
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Our current understanding of the primate cerebral cortex (neocortex) and in particular the posterior, sensory association cortex has matured to a point where it is possible to develop a family of graphical models that capture the structure, scale and power of the neocortex for purposes of associative recall, sequence prediction and pattern completion among other functions. Implementing such models using readily available computing clusters is now within the grasp of many labs and would provide scientists with the opportunity to experiment with both hard-wired connection schemes and structure-learning algorithms inspired by animal learning and developmental studies. While neural circuits involving structures external to the neocortex such as the thalamic nuclei are less well understood, the availability of a computational model on which to test hypotheses would likely accelerate our understanding of these circuits. Furthermore, the existence of an agreed-upon cortical substrate would not only facilitate our understanding of the brain but enable researchers to combine lessons learned from biology with state-of-the-art graphical-model and machine-learning techniques to design hybrid systems that combine the best of biological and traditional computing approaches.