A vision-driven model of hippocampal place cells and temporally asymmetric LTP-induction for action learning
Angelo Arleo · 1999
We describe a hippocampal neural model in which spatio-temporal features of the environment are extracted by visually driven neurons. The neuronal firing 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 field representation providing a navigational map. We present experimental results obtained by testing our model with the mobile Khepera robot. 1 Introduction Navigation of perceptually complex animals relies on (more or less explicit) spatial representation. The Hippocampus is a much-studied example of a neurophysiological structure with such a spatial representation property. Based on experimental evidence for spatially-tuned neurons (place cells) in rat hippocampus [14], it has been sugge...