Simultaneous self-organization of place and direction selectivity in a neural model of self-localization
We present a novel approach to the problem of generating internal representations that are predictive for future stimulations of an autonomous agent. In our network laterally propagated activities provide expectancy biases for subsequent activations. Activations are driven by inputs from sensors for own actions and the environment. During learning sensory input connections are adapted by Hebbian learning. From simulating a robot in different mazes we show that the units develop place fields which resemble cells in the hippocampus of rats. This implies that a precise representation of space has emerged from ambiguous sensory cues and without explicit path integration.
self-localization internal representations autonomous agents place fields hidden markov model