Recognition Automata Based on Selective Neural Networks

  • George N. ReekeJr.
  • Gerald M. Edelman

Abstract

The critical requirement for learning and other higher mental functions is the prior ability to categorize objects and events based on sensory signals reaching the brain. The theory of neuronal group selection provides an explanation for this ability based on a kind of Darwinian selection operating in somatic time on groups of interconnected neurons. These groups are formed during development with varied and overlapping abilities to respond to patterns of environmental stimulation. They are connected in networks to form repertoires of recognizing elements, and these repertoires, many of which are arranged in maps, are further arranged in parallel hierarchies which communicate with each other to carry out categorization with generalization according to attributes of adaptive significance to the organism. A key feature of such systems required to maintain spatiotemporal continuity is reentry of output signals, both within the system and globally, via changes in sensory input resulting from interactions of the system with the environment. These forms of reentry provide a basis for context-dependent figure/ground discrimination and for perceptual invariance to object transformations such as those resulting from motion.

Another key element of the selective paradigm is a form of differential amplification of groups which contribute to responses of adaptive value. In selective neural networks, such groups are selectively modified to enhance the organism’s response to future instances of the same or similar stimuli. This modification consists of enduring changes in the efficacies of the synaptic connections between cells. Rules for these changes based on known biochemical and biophysical properties of neurons have been devised and their behavioral properties studied.

Working computer models of categorizing automata based on these principles have been constructed. Examples are presented to demonstrate their ability to carry out a variety of tasks involving recognition, categorization, generalization, and visual tracking. The computer models give insight into how biological pattern recognizing systems might operate and point the way toward construction of improved recognition automata.

Keywords

Visual Tracking Selective Amplification Connection Strength Stimulus Object Repertoire Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Press, New York 1986

Authors and Affiliations

  • George N. ReekeJr.
    • 1
  • Gerald M. Edelman
    • 1
  1. 1.The Rockefeller UniversityNew YorkUSA

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