A Ventral Visual Stream Reading Center Independent of Sensory Modality and Visual Experience

AbstractFace-selective neurons are observed in the primate visual pathway and are considered as the basis of face detection in the brain. However, it has been debated as to whether this neuronal selectivity can arise innately or whether it requires training from visual experience. Here, using a hierarchical deep neural network model of the ventral visual stream, we suggest a mechanism in which face-selectivity arises in the complete absence of training. We found that units selective to faces emerge robustly in randomly initialized networks and that these units reproduce many characteristics observed in monkeys. This innate selectivity also enables the untrained network to perform face-detection tasks. Intriguingly, we observed that units selective to various non-face objects can also arise innately in untrained networks. Our results imply that the random feedforward connections in early, untrained deep neural networks may be sufficient for initializing primitive visual selectivity.

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Using Convolutional Neural Networks to measure the contribution of visual features to the representation of object animacy in the brain

10.31237/osf.io/fxz4q ◽

2019 ◽

Cited By ~ 1

Author(s):

Sushrut Thorat

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Temporal Cortex ◽

Large Degree ◽

Visual Features ◽

Visual Feature ◽

Visual Stream ◽

Feature Information ◽

Ventral Visual Stream ◽

Animal Images

A mediolateral gradation in neural responses for images spanning animals to artificial objects is observed in the ventral temporal cortex (VTC). Which information streams drive this organisation is an ongoing debate. Recently, in Proklova et al. (2016), the visual shape and category (“animacy”) dimensions in a set of stimuli were dissociated using a behavioural measure of visual feature information. fMRI responses revealed a neural cluster (extra-visual animacy cluster - xVAC) which encoded category information unexplained by visual feature information, suggesting extra-visual contributions to the organisation in the ventral visual stream. We reassess these findings using Convolutional Neural Networks (CNNs) as models for the ventral visual stream. The visual features developed in the CNN layers can categorise the shape-matched stimuli from Proklova et al. (2016) in contrast to the behavioural measures used in the study. The category organisations in xVAC and VTC are explained to a large degree by the CNN visual feature differences, casting doubt over the suggestion that visual feature differences cannot account for the animacy organisation. To inform the debate further, we designed a set of stimuli with animal images to dissociate the animacy organisation driven by the CNN visual features from the degree of familiarity and agency (thoughtfulness and feelings). Preliminary results from a new fMRI experiment designed to understand the contribution of these non-visual features are presented.

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Expectation and Surprise Determine Neural Population Responses in the Ventral Visual Stream

Journal of Neuroscience ◽

10.1523/jneurosci.2770-10.2010 ◽

2010 ◽

Vol 30 (49) ◽

pp. 16601-16608 ◽

Cited By ~ 185

Author(s):

T. Egner ◽

J. M. Monti ◽

C. Summerfield

Keyword(s):

Neural Population ◽

Visual Stream ◽

Population Responses ◽

Ventral Visual Stream

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Orthographic processing deficits in developmental dyslexia: Beyond the ventral visual stream

NeuroImage ◽

10.1016/j.neuroimage.2016.01.014 ◽

2016 ◽

Vol 128 ◽

pp. 316-327 ◽

Cited By ~ 32

Author(s):

Marianna Boros ◽

Jean-Luc Anton ◽

Catherine Pech-Georgel ◽

Jonathan Grainger ◽

Marcin Szwed ◽

...

Keyword(s):

Developmental Dyslexia ◽

Orthographic Processing ◽

Visual Stream ◽

Ventral Visual Stream ◽

Processing Deficits

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Predictive coding of action intentions in dorsal and ventral visual stream is based on visual anticipations, memory-based information and motor preparation

10.1101/480590 ◽

2018 ◽

Author(s):

Simona Monaco ◽

Giulia Malfatti ◽

Alessandro Zendron ◽

Elisa Pellencin ◽

Luca Turella

Keyword(s):

Visual Information ◽

Visual Memory ◽

Visual Recognition ◽

Predictive Coding ◽

Action Planning ◽

Neural Signals ◽

Motor Representation ◽

Visual Stream ◽

Ventral Visual Stream ◽

Action Intention

AbstractPredictions of upcoming movements are based on several types of neural signals that span the visual, somatosensory, motor and cognitive system. Thus far, pre-movement signals have been investigated while participants viewed the object to be acted upon. Here, we studied the contribution of information other than vision to the classification of preparatory signals for action, even in absence of online visual information. We used functional magnetic resonance imaging (fMRI) and multivoxel pattern analysis (MVPA) to test whether the neural signals evoked by visual, memory-based and somato-motor information can be reliably used to predict upcoming actions in areas of the dorsal and ventral visual stream during the preparatory phase preceding the action, while participants were lying still. Nineteen human participants (nine women) performed one of two actions towards an object with their eyes open or closed. Despite the well-known role of ventral stream areas in visual recognition tasks and the specialization of dorsal stream areas in somato-motor processes, we decoded action intention in areas of both streams based on visual, memory-based and somato-motor signals. Interestingly, we could reliably decode action intention in absence of visual information based on neural activity evoked when visual information was available, and vice-versa. Our results show a similar visual, memory and somato-motor representation of action planning in dorsal and ventral visual stream areas that allows predicting action intention across domains, regardless of the availability of visual information.

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Parallel processing of colors and faces in human ventral visual stream: functional evidence and technical challenges

Journal of Vision ◽

10.1167/14.10.985 ◽

2014 ◽

Vol 14 (10) ◽

pp. 985-985

Author(s):

R. Lafer-Sousa ◽

A. Kell ◽

A. Takahashi ◽

J. Feather ◽

B. Conway ◽

...

Keyword(s):

Parallel Processing ◽

Visual Stream ◽

Ventral Visual Stream ◽

Technical Challenges

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Image content is more important than Bouma’s Law for scene metamers

eLife ◽

10.7554/elife.42512 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 8

Author(s):

Thomas SA Wallis ◽

Christina M Funke ◽

Alexander S Ecker ◽

Leon A Gatys ◽

Felix A Wichmann ◽

...

Keyword(s):

Perceptual Experience ◽

Receptive Fields ◽

Natural Images ◽

Retinal Eccentricity ◽

High Fidelity ◽

Prior Work ◽

Visual Stream ◽

Area V2 ◽

Ventral Visual Stream ◽

Stream Model

We subjectively perceive our visual field with high fidelity, yet peripheral distortions can go unnoticed and peripheral objects can be difficult to identify (crowding). Prior work showed that humans could not discriminate images synthesised to match the responses of a mid-level ventral visual stream model when information was averaged in receptive fields with a scaling of about half their retinal eccentricity. This result implicated ventral visual area V2, approximated ‘Bouma’s Law’ of crowding, and has subsequently been interpreted as a link between crowding zones, receptive field scaling, and our perceptual experience. However, this experiment never assessed natural images. We find that humans can easily discriminate real and model-generated images at V2 scaling, requiring scales at least as small as V1 receptive fields to generate metamers. We speculate that explaining why scenes look as they do may require incorporating segmentation and global organisational constraints in addition to local pooling.

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