scholarly journals Learning a model of shape selectivity in V4 cells reveals shape encoding mechanisms in the brain

2021 ◽  
Vol 21 (9) ◽  
pp. 1910
Author(s):  
Paria Mehrani ◽  
John K. Tsotsos
2007 ◽  
Vol 97 (1) ◽  
pp. 307-319 ◽  
Author(s):  
Sidney R. Lehky ◽  
Anne B. Sereno

Ventral and dorsal visual pathways perform fundamentally different functions. The former is involved in object recognition, whereas the latter carries out spatial localization of stimuli and visual guidance of motor actions. Despite the association of the dorsal pathway with spatial vision, recent studies have reported shape selectivity in the dorsal stream. We compared shape encoding in anterior inferotemporal cortex (AIT), a high-level ventral area, with that in lateral intraparietal cortex (LIP), a high-level dorsal area, during a fixation task. We found shape selectivities of individual neurons to be greater in anterior inferotemporal cortex than in lateral intraparietal cortex. At the neural population level, responses to different shapes were more dissimilar in AIT than LIP. Both observations suggest a greater capacity in AIT for making finer shape distinctions. Multivariate analyses of AIT data grouped together similar shapes based on neural population responses, whereas such grouping was indistinct in LIP. Thus in a first comparison of shape response properties in late stages of the two visual pathways, we report that AIT exhibits greater capability than LIP for both object discrimination and generalization. These differences in the two visual pathways provide the first neurophysiological evidence that shape encoding in the dorsal pathway is distinct from and not a mere duplication of that formed in the ventral pathway. In addition to shape selectivity, we observed stimulus-driven cognitive effects in both areas. Stimulus repetition suppression in LIP was similar to the well-known repetition suppression in AIT and may be associated with the “inhibition of return” memory effect observed during reflexive attention.


2012 ◽  
Vol 108 (5) ◽  
pp. 1299-1308 ◽  
Author(s):  
Brittany N. Bushnell ◽  
Anitha Pasupathy

Neurons in primate cortical area V4 are sensitive to the form and color of visual stimuli. To determine whether form selectivity remains consistent across colors, we studied the responses of single V4 neurons in awake monkeys to a set of two-dimensional shapes presented in two different colors. For each neuron, we chose two colors that were visually distinct and that evoked reliable and different responses. Across neurons, the correlation coefficient between responses in the two colors ranged from −0.03 to 0.93 (median 0.54). Neurons with highly consistent shape responses, i.e., high correlation coefficients, showed greater dispersion in their responses to the different shapes, i.e., greater shape selectivity, and also tended to have less eccentric receptive field locations; among shape-selective neurons, shape consistency ranged from 0.16 to 0.93 (median 0.63). Consistency of shape responses was independent of the physical difference between the stimulus colors used and the strength of neuronal color tuning. Finally, we found that our measurement of shape response consistency was strongly influenced by the number of stimulus repeats: consistency estimates based on fewer than 10 repeats were substantially underestimated. In conclusion, our results suggest that neurons that are likely to contribute to shape perception and discrimination exhibit shape responses that are largely consistent across colors, facilitating the use of simpler algorithms for decoding shape information from V4 neuronal populations.


2017 ◽  
Vol 10 (13) ◽  
pp. 267
Author(s):  
Ankush Rai ◽  
Jagadeesh Kannan R

The process of coding information for face recognition in human is largely remaining unknown. In this study, we carry out few experiments todetermine the factors influencing coding mechanism in parahippocampal place area of the brain. The results show some significant outcome towardthe shape selectivity of the brain and latter we construct a computational mechanism to mimic the coding features behind face recognition.


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