Fig 1. Simplified generative model and neural inference network.

(A) In a simplified model, we consider visual scenes composed of two horizontally aligned, separate image patches which are encoded by their sparse representation a^u, a^v via local features Φ and non-local dependencies C. The highlighted regions indicate how particular pairs of local features may co-occur due to the long-range dependencies induced by spatially extended objects. (B) Inference in the simplified generative model can be performed by a neural population dynamics (22) whose activities represent the coefficients a^u, a^v and b^u, b^v. The corresponding neural circuit involves feedforward, recurrent, and feedback interactions which are functions of the dictionary Φ and of the long-range dependencies C.