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Constrained inference in sparse coding reproduces contextual effects and predicts laminar neural dynamics

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 au, av 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 au, av and bu, bv. The corresponding neural circuit involves feedforward, recurrent, and feedback interactions which are functions of the dictionary Φ and of the long-range dependencies C.

Fig 1