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.
- Hippocampal formation
- KIII and KIV models
- Spatio-temporal neurodynamics
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence