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Abstract

This chapter presents the first comprehensive high-level theory of the information processing function of mammalian cortex and thalamus; herein viewed as a unary structure. The theory consists of four major elements: two novel associative memory neuronal network structures (feature attractor networks and antecedent support networks), a universal information processing operation (consensus building), and an overall real-time brain control system (the brain command loop). One important derived type of thalamocortical neural network is also presented, the hierarchical abstractor (which, as with all other networks of thalamocortex, is “constructed” out of antecedent support and feature attractor networks). Some smaller constructs are also introduced. Arguments are presented as to why this theory must be basically correct. READER WARNING: The content of this chapter is complicated and almost entirely novel and unfamiliar. Detailed study and multiple readings may be required. Effort expended in learning its content will be richly rewarded. Carrying out computer experiments can be a useful learning adjunct. For simplicity and readability, the theory is presented as fact, without constant recitation of disclaimers such as “it is hypothesized.”

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Hecht-Nielsen, R. (2003). A Theory of Thalamocortex. In: Hecht-Nielsen, R., McKenna, T. (eds) Computational Models for Neuroscience. Springer, London. https://doi.org/10.1007/978-1-4471-0085-0_4

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  • DOI: https://doi.org/10.1007/978-1-4471-0085-0_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-593-9

  • Online ISBN: 978-1-4471-0085-0

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