Biologically Plausible Illusionary Contrast Perception with Spiking Neural Networks

Illusionary visual perception has been long used to shed light on biological vision pathways and mechanisms. In this work, we propose a biologically plausible spiking neural network with which spike events are used for iterative image reconstruction in which illusionary contrast perception, long known to manifest in human vision, is apparent. This parametric implementation allows us to examine this visual phenomenon in a biologically plausible computational framework, which may also account for differences in individual visual perception.