arXiv subjects

Rajani Raman

Publications and source records attributed to Rajani Raman.

2 recordsLinked to original sources

arXiv2017

Significance of Natural Scene Statistics in Understanding the Anisotropies of Perceptual Filling-in at the Blind Spot

Psychophysical experiments reveal our horizontal preference in perceptual filling-in at the blind spot. On the other hand, vertical preference is exhibited in the case of tolerance in filling-in. What causes this anisotropy in our perception? Building upon the general notion, that the functional properties of the early visual system are shaped by the innate specification as well as the statistics of the environment, we reasoned that the anisotropy in filling-in could be understood in terms of anisotropy in orientation distribution inherent to natural scene statistics. We examined this proposition by investigating filling-in of bar stimuli on a Hierarchical Predictive Coding model network. In response to bar stimuli, the model network, trained with natural images, exhibited anisotropic filling-in performance at the blind spot similar to reported in psychophysical experiments i.e. horizontal preference in filling-in and vertical preference in tolerance of filling-in. We suggest that the over-representation of horizontal contours in the natural scene contribute to the observed horizontal superiority while the broader distribution of vertical contours contributes to the observed vertical superiority in tolerance. These results indicate that natural scene statistics plays a significant role in determining the filling-in performance at the blind spot and shaping the associated anisotropies.

q-bio.NC↗

arXiv2015

Predictive coding: A Possible Explanation of Filling-in at the blind spot

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.

q-bio.NC↗