Scaling analysis of a neocortex inspired cognitive model on the Cray XD1
The Journal of Supercomputing
Spatio-temporal memories for machine learning: a long-term memory organization
IEEE Transactions on Neural Networks
Challenging computer software frontiers and the human resistance to change
Intelligent Decision Technologies - Engineering and management of IDTs for knowledge management systems
Bionic model for control platforms
Proceedings of the 7th International Conference on Frontiers of Information Technology
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This paper discusses a theory of the neocortical algorithm called the hierarchical temporal memory (HTM). Hierarchical temporal memories are built around a hierarchy of nodes. The hierarchy and how it works are the most important features of HTM theory. In an HTM, knowledge is distributed across many nodes up and down the hierarchy. As an HTM is trained, the low-level nodes learn first. Representations in high-level nodes then share what was previously learned in low-level nodes