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.

Biologically plausible computational modeling of visual perception has the potential to link high-level visual experiences to their underlying neurons' spiking dynamic. In this work, we propose a neuromorphic (brain-inspired) Spiking Neural Network (SNN)-driven model for the reconstruction of colorful images from retinal inputs. We compared our results to experimentally obtained V1 neuronal activity maps in a macaque monkey using voltage-sensitive dye imaging and used the model to demonstrate and critically explore color constancy, color assimilation, and ambiguous color perception. Our parametric implementation allows critical evaluation of visual phenomena in a single biologically plausible computational framework. It uses a parametrized combination of high and low pass image filtering and SNN-based filling-in Poisson processes to provide adequate color image perception while accounting for differences in individual perception.