1 Calibration of a Visual System with Receptor Drop - out

Maloney and Ahumada [9] have proposed a network learning algorithm that allows the visual system to compensate for irregularities in the positions of its photoreceptors. Weights in the network are adjusted by a process tending to make the internal image representation translationinvariant. We report on the behavior of this translation-invariance algorithm calibrating a visual system that has lost receptors. To attain robust performance in the presence of aliasing noise, the learning adjustment was limited to the receptive eld of output units whose receptors were lost. With this modi cation the translation-invariance learning algorithm provides a physiologically plausible model for solving the recalibration problem posed by retinal degeneration.