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arXiv:1509.08255cs.NE

Encoding Reality: Prediction-Assisted Cortical Learning Algorithm in Hierarchical Temporal Memory

Fergal Byrne

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Abstract

In the decade since Jeff Hawkins proposed Hierarchical Temporal Memory (HTM) as a model of neocortical computation, the theory and the algorithms have evolved dramatically. This paper presents a detailed description of HTM's Cortical Learning Algorithm (CLA), including for the first time a rigorous mathematical formulation of all aspects of the computations. Prediction Assisted CLA (paCLA), a refinement of the CLA is presented, which is both closer to the neuroscience and adds significantly to the computational power. Finally, we summarise the key functions of neocortex which are expressed in paCLA implementations.

arXiv comments
Updated reference to unofficial revision of Hawkins and Ahmad, 2011
Journal reference
DOI
Subjects
cs.NENeural and Evolutionary Computing cs.AIArtificial Intelligence

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  1. Study urges caution when comparing neural networks to the brain 1 comment

    • We've been doing significant research using HTM[1][2][3][4] w/ SDR[5] similar to the proprietary implementation of Semantic Folding[6] and were able to classify literally gigabytes of documents per second on FPGAs. HTM aims to resemble the architecture of the Neocortex. I simply…

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