A hierarchical attention-based neural network architecture, based on human brain guidance, for perception, conceptualisation, action and reasoning

John G. Taylor, Mathew Hartley, Neil Taylor, Christo Panchev, Statis Kasderidis

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

We present a neural network software architecture, guided by that of the human and more generally primate brain, for the construction of an autonomous cognitive system (which we have named GNOSYS). GNOSYS is created so as to be able to attend to stimuli, to conceptualise them, to learn their predicted reward value and reason about them so as to attain those stimuli in the environment with greatest predicted value. We apply this software system to an embodied version in a robot, and describe the activities in the various component modules of GNOSYS, as well as the overall results. We briefly compare our system with some others proposed to have cognitive powers, and finish by discussion of future developments we propose for our system, as well as expanding on the arguments for and against our approach to creating such a software system.
Original languageEnglish
Pages (from-to)1641-1657
Number of pages17
JournalImage and Vision Computing
Volume27
Issue number11
DOIs
Publication statusPublished - 2 Oct 2009
Externally publishedYes

Keywords

  • Dorsal and ventral vision
  • Object representations
  • Dopamine as reward
  • TD learning

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