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Hierarchical Temporal DNN and Associative Knowledge Representation

EasyChair Preprint no. 7921, version 3

Versions: 123history
22 pagesDate: August 1, 2022


This paper proposes two models. The first one is designed bottom-up, i.e., mostly based on DL and Jeff Hawkins' temporal principle. The second one tackles some aspects of intelligence, specifically concerning the thinking process. It is designed top-down, i.e., mainly based on cognition and communication.

Additionally, this paper not only exhibits top-down verse bottom-up approaches, but also presents the two edges of evolution: the DL model considers the beginning state of learning, while the knowledge representation model considers the saturated/mature/final state of learning.

Keyphrases: associative thinking, deep learning, Neuro Science

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Shimon Komarovsky},
  title = {Hierarchical Temporal DNN and Associative Knowledge Representation},
  howpublished = {EasyChair Preprint no. 7921},

  year = {EasyChair, 2022}}
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