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arXiv:1412.7955cs.NE

Unsupervised Learning through Prediction in a Model of Cortex

Christos H. Papadimitriou, Santosh S. Vempala

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Abstract

We propose a primitive called PJOIN, for "predictive join," which combines and extends the operations JOIN and LINK, which Valiant proposed as the basis of a computational theory of cortex. We show that PJOIN can be implemented in Valiant's model. We also show that, using PJOIN, certain reasonably complex learning and pattern matching tasks can be performed, in a way that involves phenomena which have been observed in cognition and the brain, namely memory-based prediction and downward traffic in the cortical hierarchy.

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DOI
Subjects
cs.NENeural and Evolutionary Computing cs.DSData Structures and Algorithms q-bio.NCNeurons and Cognition stat.MLMachine Learning (Statistics)

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    • Deep learning is mostly irrelevant for AGI but the best part of the article is bringing up the "recursive process called Merge”. This Merge [0] is called "chunking" in cognitive psychology [1, 2], first mentioned in classic paper "The Magical Number Seven" by George A. Miller…

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