Faculty Opinions recommendation of The Ventral Visual Pathway Represents Animal Appearance over Animacy, Unlike Human Behavior and Deep Neural Networks.

In recent decades, convolutional neural networks (CNNs) have delivered promising results in vision-related tasks across different domains. Previous studies have introduced deeper network architectures to further improve the performances of object classification, localization, and segmentation. However, this induces the complexity in mapping network’s layer to the processing elements in the ventral visual pathway. Although CORnet models are not precisely biomimetic, they are closer approximations to the anatomy of ventral visual pathway compared with other deep neural networks. The uniqueness of this architecture inspires us to extend it into a core object segmentation network, CORSegnet-Z. This architecture utilizes CORnet-Z building blocks as the encoding elements. We train and evaluate the proposed model using two large datasets. Our proposed model shows significant improvements on the segmentation metrics in delineating cartilage tissues from knee magnetic resonance (MR) images and segmenting lesion boundary from dermoscopic images.

Download Full-text

Contrastive Similarity Matching for Supervised Learning

Neural Computation ◽

10.1162/neco_a_01374 ◽

2021 ◽

pp. 1-29

Author(s):

Shanshan Qin ◽

Nayantara Mudur ◽

Cengiz Pehlevan

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Learning Algorithm ◽

Similarity Matrix ◽

Credit Assignment ◽

Similarity Matching ◽

Hebbian Plasticity ◽

Ventral Visual Pathway ◽

Feedback Connections ◽

Specific Learning

We propose a novel biologically plausible solution to the credit assignment problem motivated by observations in the ventral visual pathway and trained deep neural networks. In both, representations of objects in the same category become progressively more similar, while objects belonging to different categories become less similar. We use this observation to motivate a layer-specific learning goal in a deep network: each layer aims to learn a representational similarity matrix that interpolates between previous and later layers. We formulate this idea using a contrastive similarity matching objective function and derive from it deep neural networks with feedforward, lateral, and feedback connections and neurons that exhibit biologically plausible Hebbian and anti-Hebbian plasticity. Contrastive similarity matching can be interpreted as an energy-based learning algorithm, but with significant differences from others in how a contrastive function is constructed.

Download Full-text

Deep neural networks can predict human behavior in arcade games

10.32470/ccn.2019.1043-0 ◽

2019 ◽

Cited By ~ 1

Author(s):

Holger Mohr ◽

Radoslaw M. Cichy ◽

Hannes Ruge

Keyword(s):

Neural Networks ◽

Human Behavior ◽

Deep Neural Networks

Download Full-text

Comparing Object Recognition in Humans and Deep Convolutional Neural Networks—An Eye Tracking Study

Frontiers in Neuroscience ◽

10.3389/fnins.2021.750639 ◽

2021 ◽

Vol 15 ◽

Author(s):

Leonard Elia van Dyck ◽

Roland Kwitt ◽

Sebastian Jochen Denzler ◽

Walter Roland Gruber

Keyword(s):

Neural Networks ◽

Object Recognition ◽

Eye Tracking ◽

Convolutional Neural Networks ◽

Visual Pathway ◽

Proof Of Concept ◽

Deep Convolutional Neural Networks ◽

Saliency Maps ◽

Ventral Visual Pathway ◽

Specific Factors

Deep convolutional neural networks (DCNNs) and the ventral visual pathway share vast architectural and functional similarities in visual challenges such as object recognition. Recent insights have demonstrated that both hierarchical cascades can be compared in terms of both exerted behavior and underlying activation. However, these approaches ignore key differences in spatial priorities of information processing. In this proof-of-concept study, we demonstrate a comparison of human observers (N = 45) and three feedforward DCNNs through eye tracking and saliency maps. The results reveal fundamentally different resolutions in both visualization methods that need to be considered for an insightful comparison. Moreover, we provide evidence that a DCNN with biologically plausible receptive field sizes called vNet reveals higher agreement with human viewing behavior as contrasted with a standard ResNet architecture. We find that image-specific factors such as category, animacy, arousal, and valence have a direct link to the agreement of spatial object recognition priorities in humans and DCNNs, while other measures such as difficulty and general image properties do not. With this approach, we try to open up new perspectives at the intersection of biological and computer vision research.

Download Full-text

Untangling the animacy organization of occipitotemporal cortex

10.1101/2020.07.17.206896 ◽

2020 ◽

Author(s):

J. Brendan Ritchie ◽

Astrid A. Zeman ◽

Joyce Bosmans ◽

Shuo Sun ◽

Kirsten Verhaegen ◽

...

Keyword(s):

Deep Neural Networks ◽

Visual Pathway ◽

Functional Specialization ◽

Independent Contribution ◽

Ventral Visual Pathway ◽

Two Factors ◽

The Face ◽

Separate Factor ◽

Occipitotemporal Cortex ◽

Human Faces

AbstractSome of the most impressive functional specialization in the human brain is found in occipitotemporal cortex (OTC), where several areas exhibit selectivity for a small number of visual categories, such as faces and bodies, and spatially cluster based on stimulus animacy. Previous studies suggest this animacy organization reflects the representation of an intuitive taxonomic hierarchy, distinct from the presence of face- and body-selective areas in OTC. Using human fMRI, we investigated the independent contribution of these two factors – the face-body division and taxonomic hierarchy – in accounting for the animacy organization of OTC, and whether they might also be reflected in the architecture of several deep neural networks. We found that graded selectivity based on animal resemblance to human faces and bodies masquerades as an apparent animacy continuum, which suggests that taxonomy is not a separate factor underlying the organization of the ventral visual pathway.

Download Full-text