Basic principles of the KIV model and its application to the navigation problem.
EEG measurements indicate the presence of common-mode, coherent oscillations shared by multiple cortical areas. In previous studies the KIII model has been introduced, which interprets the experimental observations as nonlinear, spatially distributed dynamical oscillations of locally coupled neural populations. KIII can account for the fast and robust classification and pattern recognition in sensory cortices. In order to describe selection of action, planning, and spatial orientation functions, in this paper we expand KIII into the KIV model. KIV approximates the operation of the corticostriatal-hippocampal system. KIV consists of three KI, eight KII and three KIII components, including sensory and cortical systems, as well as the hippocampus, amygdala, and the septum. KIV implements various types of dynamic neural activities. The neural activity patterns determine the emergence of global spatial encoding to implement the orientation function of a simulated animal. Our results indicate the mechanisms, which we believe support the generation of cognitive maps in the hippocampus based on the sensory input-based destabilization of cortical spatio-temporal patterns. In this paper, we describe the conceptual design of the KIV model. We outline the biological background and motivation of the basic principles that are applied to design the KIV computational model. We use the KIV model to explain how the hippocampal neural circuitry functions are constructed and controlled by the corticostriatal-hippocampal loops, supplemented with specific subcortical units. In the second part, we implement these principles using the example of the hippocampal formation as a KIII unit. We demonstrate the learning and navigation principles using the Evolving Multi-module Mobile Agent (EMMA) in 2D software environment.
Kozma
Robert
R
Division of Computer Science, The University of Memphis, Memphis, TN 38152, USA. rkozma@memphis.edu
Freeman
Walter J
WJ
D000818:Animals; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D003342:Corpus Striatum / Q000502:physiology*; D006624:Hippocampus / Q000502:physiology*; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D012054:Reinforcement, Psychology