A hippocampal model of visually guided navigation as implemented by a mobile agent

Visually guided landmark navigation is based on space coding by hippocampal place cells (PC). A biologically realistic architecture of cooperative-competitive associative networks (implemented as a control system for mobile agents) emulates PC activity during local navigation in exploration and goal-retrieval. The system builds and stores panoramic views from landmarks and compares them with current inputs. Mismatch-induced low levels of recognition trigger a vigilance burst, which favors either the recognition of an alternative place category or the creation of a new category. The sole implementation of visual “What” and “Where” information does not restrain the generality of the model since several modalities could cooperate to give rise to more robust place field spatial categories. Providing the system with real visual inputs automatically extracted from a natural environment demonstrates that interspecies differences in PC coding result more from characteristics of the visual systems than from differences in processing. Conversely, differences in PC multiple codes within a system result from different levels of processing and/or different degrees of multimodality. Each code could be used within different navigational strategies. A control system derived from the model allows a mobile agent to learn a few places and the associated actions required to reach a goal. Generalisation property of the model provides the capacity to join the goal from any place in the learned environment