  Both neurophysiological and psychophysical experiments have pointed out the
crucial role of recurrent and feedback connections to process context-dependent
information in the early visual cortex. While numerous models have accounted
for feedback effects at either neural or representational level, none of them
were able to bind those two levels of analysis. Is it possible to describe
feedback effects at both levels using the same model? We answer this question
by combining Predictive Coding (PC) and Sparse Coding (SC) into a hierarchical
and convolutional framework. In this Sparse Deep Predictive Coding (SDPC)
model, the SC component models the internal recurrent processing within each
layer, and the PC component describes the interactions between layers using
feedforward and feedback connections. Here, we train a 2-layered SDPC on two
different databases of images, and we interpret it as a model of the early
visual system (V1 & V2). We first demonstrate that once the training has
converged, SDPC exhibits oriented and localized receptive fields in V1 and more
complex features in V2. Second, we analyze the effects of feedback on the
neural organization beyond the classical receptive field of V1 neurons using
interaction maps. These maps are similar to association fields and reflect the
Gestalt principle of good continuation. We demonstrate that feedback signals
reorganize interaction maps and modulate neural activity to promote contour
integration. Third, we demonstrate at the representational level that the SDPC
feedback connections are able to overcome noise in input images. Therefore, the
SDPC captures the association field principle at the neural level which results
in better disambiguation of blurred images at the representational level.
