Computational modeling of spatiotemporal afterimage visual perception with spiking neural networks

Contour-induced afterimages constitute an important class of chromatic visual illusions, in which an illusory color percept emerges post-exposure to a chromatic field. Their striking feature is dual polarity (the perception of both complementary and inducer hues) and the capacity for extending to naive, non-adapted regions, indicating the involvement of neural mechanisms that extend beyond established models of simple neural adaptation. In this work, we realized the perceptual afterimage effect with a biologically plausible spiking neural network. We compared the results with experimental findings with human participants, demonstrating how a complex temporal evolution of a visual illusion can emerge from the dynamics of its constituent spiking dynamics. Our neural design models a wide range of phenomena, including positive, negative, and combined afterimage configurations, as well as the effects of alternating and open contours. By intrinsically incorporating the temporal dimension through its spiking dynamics, the model accurately reproduces the temporal evolution of the perceived color, including the alternating polarity observed with successive contours. We show that a single, unified, and biologically plausible spiking architecture can account for both veridical color and the complex set of contour-induced afterimage phenomena, suggesting that a common, active neural process, chromatic filling-in, is responsible for the different forms of perceived color. From an engineering perspective, our model exemplifies neuromorphic computational processing of event-based representations of visual data without reducing to static frames, and enables systematic analysis of inference error and illusory afterimages through configurable parameters, offering conceptual guidance for designing bio-inspired neuromorphic imaging pipelines.