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.Comment: 23 pages, 9 figure

Silicate solubilizing and plant growth promoting bacteria interact with biogenic silica to impart heat stress tolerance in rice by modulating physiology and gene expression

Heat stress caused due to increasing warming climate has become a severe threat to global food production including rice. Silicon plays a major role in improving growth and productivity of rice by aiding in alleviating heat stress in rice. Soil silicon is only sparingly available to the crops can be made available by silicate solubilizing and plant-growth-promoting bacteria that possess the capacity to solubilize insoluble silicates can increase the availability of soluble silicates in the soil. In addition, plant growth promoting bacteria are known to enhance the tolerance to abiotic stresses of plants, by affecting the biochemical and physiological characteristics of plants. The present study is intended to understand the role of beneficial bacteria viz. Rhizobium sp. IIRR N1 a silicate solublizer and Gluconacetobacter diazotrophicus, a plant growth promoting bacteria and their interaction with insoluble silicate sources on morpho-physiological and molecular attributes of rice (Oryza sativa L.) seedlings after exposure to heat stress in a controlled hydroponic system. Joint inoculation of silicates and both the bacteria increased silicon content in rice tissue, root and shoot biomass, significantly increased the antioxidant enzyme activities (viz. superoxidase dismutase, catalase and ascorbate peroxidase) compared to other treatments with sole application of either silicon or bacteria. The physiological traits (viz. chlorophyll content, relative water content) were also found to be significantly enhanced in presence of silicates and both the bacteria after exposure to heat stress conditions. Expression profiling of shoot and root tissues of rice seedlings revealed that seedlings grown in the presence of silicates and both the bacteria exhibited higher expression of heat shock proteins (HSPs viz., OsHsp90, OsHsp100 and 60 kDa chaperonin), hormone-related genes (OsIAA6) and silicon transporters (OsLsi1 and OsLsi2) as compared to seedlings treated with either silicates or with the bacteria alone. The results thus reveal the interactive effect of combined application of silicates along with bacteria Rhizobium sp. IIRR N1, G. diazotrophicus inoculation not only led to augmented silicon uptake by rice seedlings but also influenced the plant biomass and elicited higher expression of HSPs, hormone-related and silicon transporter genes leading to improved tolerance of seedling to heat stress

Nonlinearity in the response profile.

<p>Average normalized responses of neurons (in BS module) to various stimulus conditions are presented. Estimated responses to stimuli a, b, c and ab, as well as the sum of the responses to a and b are shown, where ab is a combination of stimuli a and b. Each stimulus is schematically shown below each bar plot, where each bar plot shows the mean of normalized responses of 8 most responsive neurons in the BS module. Conventions are same as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151194#pone.0151194.g008" target="_blank">Fig 8</a>.</p

Response profile at level 2.

<p>(a) The normalized response of 169 neurons at level 2 corresponding to shifting bar stimuli for the end postilion 6. (b) Plots of the normalized absolute value of response of most active neurons at level 2 (marked as red arrow in (a)). The receptive field of these neurons is shown in the inset of their corresponding plots.</p

General HPC architecture (adopted from Rao. [14]).

<p>a) On arrival of input, predictive estimator module at each higher visual processing level makes the estimate and sends prediction signal to its next lower level by feedback connection and receives the corresponding prediction error by a feed-forward connection. The error signal is used by the predictive estimator module to correct the estimate for better prediction. b) General predictive estimator (PE) module constitutes of (i) neurons to represent the estimate of the input I by their response vector r by minimizing the bottom-up (<b>I</b> − <i>U</i><b>r</b>) and top-down (<b>r</b> − <b>r</b>^<i>td</i>) error, (ii) feed-forward error carrying neurons has the efficacy matrix <i>U</i>, which encode the basis vectors their synaptic weights (or receptive fields), (iii) prediction <i>U</i><b>r</b> carrying neurons and (iv) top down error detecting neurons.</p

Response elevation in BS region at level 1.

<p>Plots of the absolute value of normalized response are shown against the bar position for three highly active neurons (indicated by red arrows in the sixth bar blot of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151194#pone.0151194.g007" target="_blank">Fig 7</a>) In these plots, dotted rectangular area indicates the extension of BS module whereas, the solid gray rectangular area indicates the extension of blind spot. The receptive fields of these three neurons are shown at the top of the respective plots, which show that these neurons participated in encoding information of a horizontal bar. To compare the relative activity of the neurons we have plotted the absolute value of the responses instead of signed values of responses.</p

Shifting-bar.

<p>a) A typical 30 × 30 pixel stimulus is shown here. The darkened object in the stimulus is a bar, whose endpoint is represented by the number 1. Five more stimuli were constructed by shifting the bar end to positions 2 to 6. The larger rectangle of size 12 × 12 pixels (shown by the dotted line at the center) indicates the extension of BS module and the smaller one of size 8 × 8 (shown by the solid line) indicate the extension of blind spot. b) Generated 30 × 30 “perceptual images” corresponding to response profile of PE neurons at level 1 of the HPC network for non-BS (top row) and BS (bottom row) cases are shown.</p

Responses profiles.

<p>Normalized responses of 64 PE neurons at BS module, corresponding to the six stimuli discussed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151194#pone.0151194.g006" target="_blank">Fig 6a</a> are presented. The dark blue bar represents the response of PE neurons for the BS network, whereas, the light blue bar represents the responses for the non-BS network. Three most highly active neurons (in bottom leftmost bar plot) are marked by red arrows.</p