Self-Localization by Hidden Representations
We present a framework for generating representations of space in an autonomous agent which does not obtain any direct information about its location. Instead the algorithm relies exclusively on sensory input and internal estimations of actions. The activations within a neural network are propagated in time depending on internal estimations of actions. Sensory input connections are adapted according to a Hebbian learning rules derived from the prediction error on sensory inputs one step ahead. During exploration of the environment the respective cells develop location and direction selectivity even when relying on highly ambiguous stimuli.
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