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eNeuro. 2018 May 8;5(3). pii: ENEURO.0443-17.2018. doi: 10.1523/ENEURO.0443-17.2018. eCollection 2018 May-Jun.

Sharpening of Hierarchical Visual Feature Representations of Blurred Images.

Author information

1
Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-Ku, Kyoto 606-8501, Japan.
2
ATR Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Seika, Soraku, Kyoto 619-0288, Japan.

Abstract

The robustness of the visual system lies in its ability to perceive degraded images. This is achieved through interacting bottom-up, recurrent, and top-down pathways that process the visual input in concordance with stored prior information. The interaction mechanism by which they integrate visual input and prior information is still enigmatic. We present a new approach using deep neural network (DNN) representation to reveal the effects of such integration on degraded visual inputs. We transformed measured human brain activity resulting from viewing blurred images to the hierarchical representation space derived from a feedforward DNN. Transformed representations were found to veer toward the original nonblurred image and away from the blurred stimulus image. This indicated deblurring or sharpening in the neural representation, and possibly in our perception. We anticipate these results will help unravel the interplay mechanism between bottom-up, recurrent, and top-down pathways, leading to more comprehensive models of vision.

KEYWORDS:

Decoding; Deep Neural Network; fMRI

PMID:
29756028
PMCID:
PMC5940673
DOI:
10.1523/ENEURO.0443-17.2018
[Indexed for MEDLINE]
Free PMC Article

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