Abstract
Many animals can return home accurately after exploring for food using their own homing navigation algorithm. Path integration plays a critical role in homing navigation. It is believed that animals are able to recognize their relative location from the nest by accumulating both distance and direction experienced during their travel. We tested possible patterns of neuronal organization for a path integration mechanism. The neural networks consisted of a circular array of neurons, following population coding. We describe here a neural model of path integration involving a relatively small number of neurons and discuss how well the model operates for homing navigation. Robotic simulations suggest that a neural structure with only a few sensor neurons can successfully handle the path integration needed for homing navigation.
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