RT Journal Article
SR Electronic
T1 Learning of Active Binocular Vision in a Biomechanical Model of the Oculomotor System
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 160721
DO 10.1101/160721
A1 Lukas Klimmasch
A1 Alexander Lelais
A1 Alexander Lichtenstein
A1 Bertram E. Shi
A1 Jochen Triesch
YR 2017
UL http://biorxiv.org/content/early/2017/07/07/160721.abstract
AB We present a model for the autonomous learning of active binocular vision using a recently developed biome-chanical model of the human oculomotor system. The model is formulated in the Active Efficient Coding (AEC) framework, a recent generalization of classic efficient coding theories to active perception. The model simultaneously learns how to efficiently encode binocular images and how to generate accurate vergence eye movements that facilitate efficient encoding of the visual input. In order to resolve the redundancy problem arising from the actuation of the eyes through antagonistic muscle pairs, we consider the metabolic costs associated with eye movements. We show that the model successfully learns to trade off vergence accuracy against the associated metabolic costs, producing high fidelity vergence eye movements obeying Sherrington’s law of reciprocal innervation.