Abstract-Several theories of early visual perception hypothesize neural circuits that are responsible for assigning ownership of an object's occluding contour to a region which represents the "figure." Previously, we have presented a Bayesian network model which integrates multiple cues and uses belief propagation to infer local figure-ground relationships along an object's occluding contour. In this paper, we use a linear integrate-and-fire model to demonstrate how such inference mechanisms could be carried out in a biologically realistic neural circuit. The circuit maps the membrane potentials of individual neurons to log probabilities and uses recurrent connections to represent transition probabilities. The network's "perception" of figure-ground is demonstrated for several examples, including perceptually ambiguous figures, and compared qualitatively and quantitatively with human psychophysics. Index Terms-Cortical hypercolumn, figure-ground, integrateand-fire, probabilistic inference, visual perception.
In REF , a neurally plausible model integrating multiple figure-ground cues using belief propagation in Bayesian networks with leaky integrate and fire neurons was proposed.
Inferring figure-ground using a recurrent integrate-and-fire neural circuit
IEEE Transactions on Neural Systems and Rehabilitation Engineering
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Psychology Medicine