Simulating consciousness in a bilateral neural network: "nuclear" and "fringe" awareness

Conscious Cogn. 1999 Mar;8(1):62-93. doi: 10.1006/ccog.1998.0374.

Abstract

A technique for the bilateral activation of neural nets that leads to a functional asymmetry of two simulated "cerebral hemispheres" is described. The simulation is designed to perform object recognition, while exhibiting characteristics typical of human consciousness-specifically, the unitary nature of conscious attention, together with a dual awareness corresponding to the "nucleus" and "fringe" described by William James (1890). Sensory neural nets self-organize on the basis of five sensory features. The system is then taught arbitrary symbolic labels for a small number of similar stimuli. Finally, the trained network is exposed to nonverbal stimuli for object recognition, leading to Gaussian activation of the "sensory" maps-with a peak at the location most closely related to the features of the external stimulus. "Verbal" maps are activated most strongly at the labeled location that lies closest to the peak on homologous sensory maps. On the verbal maps activation is characterized by both excitatory and inhibitory Gaussians (a Mexican hat), the parameters of which are determined by the relative locations of the verbal labels. Mutual homotopic inhibition across the "corpus callosum" then produces functional cerebral asymmetries, i.e., complementary activation of homologous "association" and "frontal" maps within a common focus of attention-a nucleus in the left hemisphere and a fringe in the right hemisphere. An object is recognized as corresponding to a known label when the total activation of both hemispheres (nucleus plus fringe) is strongest for that label. The functional dualities of the cerebral hemispheres are discussed in light of the nucleus/fringe asymmetry.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain Mapping
  • Cerebral Cortex / physiology*
  • Consciousness / physiology*
  • Functional Laterality / physiology*
  • Humans
  • Models, Neurological
  • Neural Networks, Computer*
  • Psychophysiology