scholarly journals Can Object Category-Selectivity in the Ventral Visual Pathway Be Explained by Sensitivity to Low-Level Image Properties?

2014 ◽  
Vol 34 (45) ◽  
pp. 14817-14819 ◽  
Author(s):  
S. G. Wardle ◽  
J. B. Ritchie
Keyword(s):  
Visual Pathway ◽  
Object Category ◽  
Low Level ◽  
Ventral Visual Pathway

scholarly journals View-tuned and view-invariant face encoding in IT cortex is explained by selected natural image fragments

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yunjun Nam ◽  
Takayuki Sato ◽  
Go Uchida ◽  
Ekaterina Malakhova ◽  
Shimon Ullman ◽  
...  
Keyword(s):  
Face Recognition ◽  
Visual Pathway ◽  
Visual Features ◽  
Natural Image ◽  
Face Representation ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
Neural Substrate ◽  
The Face

AbstractHumans recognize individual faces regardless of variation in the facial view. The view-tuned face neurons in the inferior temporal (IT) cortex are regarded as the neural substrate for view-invariant face recognition. This study approximated visual features encoded by these neurons as combinations of local orientations and colors, originated from natural image fragments. The resultant features reproduced the preference of these neurons to particular facial views. We also found that faces of one identity were separable from the faces of other identities in a space where each axis represented one of these features. These results suggested that view-invariant face representation was established by combining view sensitive visual features. The face representation with these features suggested that, with respect to view-invariant face representation, the seemingly complex and deeply layered ventral visual pathway can be approximated via a shallow network, comprised of layers of low-level processing for local orientations and colors (V1/V2-level) and the layers which detect particular sets of low-level elements derived from natural image fragments (IT-level).

scholarly journals Reconstructing feedback representations in ventral visual pathway with a generative adversarial autoencoder

2020 ◽  
Author(s):  
Haider Al-Tahan ◽  
Yalda Mohsenzadeh
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Visual Pathway ◽  
Functional Magnetic Resonance ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
High Level ◽  
Feedback Connections ◽  
The Brain ◽  
Insight Into

AbstractWhile vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational similarity of the activity patterns in the proposed model with temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) visual brain responses. The proposed generative model identified two segregated neural dynamics in the visual brain. A temporal hierarchy of processes transforming low level visual information into high level semantics in the feedforward sweep, and a temporally later dynamics of inverse processes reconstructing low level visual information from a high level latent representation in the feedback sweep. Our results append to previous studies on neural feedback processes by presenting a new insight into the algorithmic function and the information carried by the feedback processes in the ventral visual pathway.Author summaryIt has been shown that the ventral visual cortex consists of a dense network of regions with feedforward and feedback connections. The feedforward path processes visual inputs along a hierarchy of cortical areas that starts in early visual cortex (an area tuned to low level features e.g. edges/corners) and ends in inferior temporal cortex (an area that responds to higher level categorical contents e.g. faces/objects). Alternatively, the feedback connections modulate neuronal responses in this hierarchy by broadcasting information from higher to lower areas. In recent years, deep neural network models which are trained on object recognition tasks achieved human-level performance and showed similar activation patterns to the visual brain. In this work, we developed a generative neural network model that consists of encoding and decoding sub-networks. By comparing this computational model with the human brain temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) response patterns, we found that the encoder processes resemble the brain feedforward processing dynamics and the decoder shares similarity with the brain feedback processing dynamics. These results provide an algorithmic insight into the spatiotemporal dynamics of feedforward and feedback processes in biological vision.

scholarly journals Oscillatory Dynamics of Perceptual to Conceptual Transformations in the Ventral Visual Pathway

2018 ◽  
Vol 30 (11) ◽  
pp. 1590-1605 ◽  
Author(s):  
Alex Clarke ◽  
Barry J. Devereux ◽  
Lorraine K. Tyler
Keyword(s):  
Visual Information ◽  
Computational Models ◽  
Semantic Information ◽  
Low Frequency ◽  
Visual Pathway ◽  
Semantic Representations ◽  
Low Level ◽  
Oscillatory Dynamics ◽  
Incremental Model ◽  
Ventral Visual Pathway

Object recognition requires dynamic transformations of low-level visual inputs to complex semantic representations. Although this process depends on the ventral visual pathway, we lack an incremental account from low-level inputs to semantic representations and the mechanistic details of these dynamics. Here we combine computational models of vision with semantics and test the output of the incremental model against patterns of neural oscillations recorded with magnetoencephalography in humans. Representational similarity analysis showed visual information was represented in low-frequency activity throughout the ventral visual pathway, and semantic information was represented in theta activity. Furthermore, directed connectivity showed visual information travels through feedforward connections, whereas visual information is transformed into semantic representations through feedforward and feedback activity, centered on the anterior temporal lobe. Our research highlights that the complex transformations between visual and semantic information is driven by feedforward and recurrent dynamics resulting in object-specific semantics.

scholarly journals Low-level properties of natural images predict topographic patterns of neural response in the ventral visual pathway

2015 ◽  
Vol 15 (7) ◽  
pp. 3 ◽  
Author(s):  
Timothy J. Andrews ◽  
David M. Watson ◽  
Grace E. Rice ◽  
Tom Hartley
Keyword(s):  
Visual Pathway ◽  
Neural Response ◽  
Natural Images ◽  
Low Level ◽  
Ventral Visual Pathway

scholarly journals Oscillatory dynamics of perceptual to conceptual transformations in the ventral visual pathway

2018 ◽  
Author(s):  
Alex Clarke ◽  
Barry J. Devereux ◽  
Lorraine K. Tyler
Keyword(s):  
Visual Information ◽  
Computational Models ◽  
Semantic Information ◽  
Visual Pathway ◽  
Semantic Representations ◽  
Low Level ◽  
Oscillatory Dynamics ◽  
Incremental Model ◽  
Visual Inputs ◽  
Ventral Visual Pathway

AbstractObject recognition requires dynamic transformations of low-level visual inputs to complex semantic representations. While this process depends on the ventral visual pathway (VVP), we lack an incremental account from low-level inputs to semantic representations, and the mechanistic details of these dynamics. Here we combine computational models of vision with semantics, and test the output of the incremental model against patterns of neural oscillations recorded with MEG in humans. Representational Similarity Analysis showed visual information was represented in alpha activity throughout the VVP, and semantic information was represented in theta activity. Furthermore, informational connectivity showed visual information travels through feedforward connections, while visual information is transformed into semantic representations through feedforward and feedback activity, centered on the anterior temporal lobe. Our research highlights that the complex transformations between visual and semantic information is driven by feedforward and recurrent dynamics resulting in object-specific semantics.

scholarly journals Using neural distance to predict reaction time for categorizing the animacy, shape, and abstract properties of objects

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
J. Brendan Ritchie ◽  
Hans Op de Beeck
Keyword(s):  
Reaction Times ◽  
Visual Pathway ◽  
Neural Activation ◽  
Object Category ◽  
Asymmetric Effects ◽  
Ventral Visual Pathway ◽  
Shape Distance ◽  
Stimulus Design ◽  
High Level ◽  
The Brain

Abstract A large number of neuroimaging studies have shown that information about object category can be decoded from regions of the ventral visual pathway. One question is how this information might be functionally exploited in the brain. In an attempt to help answer this question, some studies have adopted a neural distance-to-bound approach, and shown that distance to a classifier decision boundary through neural activation space can be used to predict reaction times (RT) on animacy categorization tasks. However, these experiments have not controlled for possible visual confounds, such as shape, in their stimulus design. In the present study we sought to determine whether, when animacy and shape properties are orthogonal, neural distance in low- and high-level visual cortex would predict categorization RTs, and whether a combination of animacy and shape distance might predict RTs when categories crisscrossed the two stimulus dimensions, and so were not linearly separable. In line with previous results, we found that RTs correlated with neural distance, but only for animate stimuli, with similar, though weaker, asymmetric effects for the shape and crisscrossing tasks. Taken together, these results suggest there is potential to expand the neural distance-to-bound approach to other divisions beyond animacy and object category.

scholarly journals Using neural distance to predict reaction time for categorizing the animacy, shape, and abstract properties of objects

2018 ◽  
Author(s):  
J.Brendan Ritchie ◽  
Hans Op de Beeck
Keyword(s):  
Reaction Times ◽  
Visual Pathway ◽  
Neural Activation ◽  
Object Category ◽  
Asymmetric Effects ◽  
Ventral Visual Pathway ◽  
Shape Distance ◽  
Stimulus Design ◽  
High Level ◽  
The Brain

A large number of neuroimaging studies have shown that information about object category can be decoded from regions of the ventral visual pathway. One question is how this information might be functionally exploited in the brain. In an attempt to answer this question, some studies have adopted a neural distance-to-bound approach, and shown that distance to a classifier decision boundary through neural activation space can be used to predict reaction times (RT) on animacy categorization tasks. However, these experiments have not controlled for possible visual confounds, such as shape, in their stimulus design. In the present study we sought to determine whether, when animacy and shape properties are orthogonal, neural distance in low- and high-level visual cortex would predict categorization RTs. We also investigated whether a combination of animacy and shape distance might predict RTs when categories crisscrossed the two stimulus dimensions, and so were not linearly separable. In line with previous results, we found that RTs correlated with neural distance, but only for animate stimuli, with similar, though weaker, asymmetric effects for the shape and crisscrossing tasks. Taken together, these results suggest there is potential to expand the neural distance-to-bound approach to other divisions beyond animacy and object category.

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