A bridge-islands model for brains: Developing numeric circuits for logic and motivation

Weng, Juyang

doi:10.1109/ijcnn.2014.6889953

Cited by 3 publications

(1 citation statement)

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“…The top-k voting in Eq. ( 3) was called the "bridged-islands" model [92]. In general, the "bridge" Y area can be considered any brain area where neurons fire to be used by its connected islands -top islands Z and bottom islands X.…”

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confidence: 99%

A Model for Auto-Programming for General Purposes

Weng¹

2018

Preprint

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The Universal Turing Machine (TM) is a model for VonNeumann computers -generalpurpose computers. A human brain can inside-skull-automatically learn a universal TM so that he acts as a general-purpose computer and writes a computer program for any practical purposes. It is unknown whether a machine can accomplish the same. This theoretical work shows how the Developmental Network (DN) can accomplish this. Unlike a traditional TM, the TM learned by DN is a super TM -Grounded, Emergent, Natural, Incremental, Skulled, Attentive, Motivated, and Abstractive (GENISAMA). A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation). Its learning from a teacher TM is one transition observation at a time, immediate, and error-free until all its neurons have been initialized by early observed teacher transitions. From that point on, the DN is no longer error-free but is always optimal at every time instance in the sense of maximal likelihood, conditioned on its limited computational resources and the learning experience. This letter also extends the Church-Turing thesis to automatic programming for general purposes and sketchily proved it.

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A Model for Auto-Programming for General Purposes

Weng¹

2018

Preprint

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The short-context priority of emergent representations in unsupervised learning

Gan

Weng

2014

2014 10th International Conference on Natural Computation (ICNC)

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Autonomous Programming for General Purposes: Theory

Weng

2020

Int. J. Human. Robot.

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The universal Turing Machine (TM) is a model for Von Neumann computers — general-purpose computers. A human brain, linked with its biological body, can inside-skull-autonomously learn a universal TM so that he acts as a general-purpose computer and writes a computer program for any practical purposes. It is unknown whether a robot can accomplish the same. This theoretical work shows how the Developmental Network (DN), linked with its robot body, can accomplish this. Unlike a traditional TM, the TM learned by DN is a super TM — Grounded, Emergent, Natural, Incremental, Skulled, Attentive, Motivated, and Abstractive (GENISAMA). A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation). Its learning from a teacher TM is one transition observation at a time, immediate, and error-free until all its neurons have been initialized by early observed teacher transitions. From that point on, the DN is no longer error-free but is always optimal at every time instance in the sense of maximal likelihood, conditioned on its limited computational resources and the learning experience. This paper extends the Church–Turing thesis to a stronger version — a GENISAMA TM is capable of Autonomous Programming for General Purposes (APFGP) — and proves both the Church–Turing thesis and its stronger version.

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A bridge-islands model for brains: Developing numeric circuits for logic and motivation

Cited by 3 publications

References 29 publications

A Model for Auto-Programming for General Purposes

A Model for Auto-Programming for General Purposes

The short-context priority of emergent representations in unsupervised learning

Autonomous Programming for General Purposes: Theory

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