Learning environmental clues in the KIV model of the cortico-hippocampal formation

Robert Kozma, Walter J. Freeman, Derek Wong, Peter Erdi

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Previous studies on the KIV model outlined a general architecture of modeling sensory-perceptual-intentional action cycle in the primordial vertebrate forebrain using nonlinear dynamical principles. KIV consists of three KIII units representing aperiodic/chaotic dynamics in sensory cortex, hippocampal formation, and midline forebrain, respectively. The sensory cortex has demonstrated excellent performance as a pattern recognition and classification device. In this work, the behavior of the hippocampal formation is studied as part of the KIV system. We elaborate a reinforcement algorithm to learn goal-oriented behavior based on global orientation beacons, biased by local sensory information provided by visual or infra-red sensors. We illustrate the operation of the KIV model using the multiple T-maze navigation problem.

Original languageEnglish
Pages (from-to)721-728
Number of pages8
JournalNeurocomputing
Volume58-60
DOIs
Publication statusPublished - Jun 1 2004

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Keywords

  • Cortex
  • Hippocampal formation
  • KIII and KIV models
  • Spatio-temporal neurodynamics

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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