A Vision-driven Model of Hippocampal Place Cells and Temporally Asymmetric LTP-induction for Action Learning

We describe a hippocampal neural model in which spatio-temporal features of the environment are extracted by visually driven neu-rons. The neuronal ring activity implicitly measures properties like agent-landmark distance and egocentric orientation to visual cues. This leads to a neural representation where populations of place cells encode spatial locations within the environment. In addition, temporally asymmetric long-term potentiation of synapses between place cells is used to learn a vector eld representation providing a navi-gational map. We present experimental results obtained by testing our model with the mobile Khepera robot.