  Filling-in at the blind-spot is a perceptual phenomenon in which the visual
system fills the informational void, which arises due to the absence of retinal
input corresponding to the optic disc, with surrounding visual attributes.
Though there are enough evidence to conclude that some kind of neural
computation is involved in filling-in at the blind spot especially in the early
visual cortex, the knowledge of the actual computational mechanism is far from
complete. We have investigated the bar experiments and the associated
filling-in phenomenon in the light of the hierarchical predictive coding
framework, where the blind-spot was represented by the absence of early
feed-forward connection. We recorded the responses of predictive estimator
neurons at the blind-spot region in the V1 area of our three level (LGN-V1-V2)
model network. These responses are in agreement with the results of earlier
physiological studies and using the generative model we also showed that these
response profiles indeed represent the filling-in completion. These demonstrate
that predictive coding framework could account for the filling-in phenomena
observed in several psychophysical and physiological experiments involving bar
stimuli. These results suggest that the filling-in could naturally arise from
the computational principle of hierarchical predictive coding (HPC) of natural
images.
