Learning spatial navigation using chaotic neural network model

In this work, the KIV model is used for the description of the interaction between the sensory and cortical systems, the hippocampus, the amygdala, and the septum. Neural activity patterns in KIV determine the emergence of global spatial encoding to implement the orientation function of a simulated animal. Our results embody 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. We illustrate learning results using the example of simulated navigation in a 2D environment.