Reassessing hierarchical correspondences between brain and deep networks through direct interface
NJ Sexton, BC Love - Science advances, 2022 - science.org
Functional correspondences between deep convolutional neural networks (DCNNs) and the
mammalian visual system support a hierarchical account in which successive stages of …
mammalian visual system support a hierarchical account in which successive stages of …
Brain hierarchy score: Which deep neural networks are hierarchically brain-like?
Achievement of human-level image recognition by deep neural networks (DNNs) has
spurred interest in whether and how DNNs are brain-like. Both DNNs and the visual cortex …
spurred interest in whether and how DNNs are brain-like. Both DNNs and the visual cortex …
Individual differences among deep neural network models
Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as
a modeling framework for neural computations in the primate brain. Just like individual …
a modeling framework for neural computations in the primate brain. Just like individual …
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
The complex multi-stage architecture of cortical visual pathways provides the neural basis
for efficient visual object recognition in humans. However, the stage-wise computations …
for efficient visual object recognition in humans. However, the stage-wise computations …
Comparing object recognition in humans and deep convolutional neural networks—an eye tracking study
LE Van Dyck, R Kwitt, SJ Denzler… - Frontiers in …, 2021 - frontiersin.org
Deep convolutional neural networks (DCNNs) and the ventral visual pathway share vast
architectural and functional similarities in visual challenges such as object recognition …
architectural and functional similarities in visual challenges such as object recognition …
Brain-like object recognition with high-performing shallow recurrent ANNs
Deep convolutional artificial neural networks (ANNs) are the leading class of candidate
models of the mechanisms of visual processing in the primate ventral stream. While initially …
models of the mechanisms of visual processing in the primate ventral stream. While initially …
Deep convolutional neural networks outperform feature-based but not categorical models in explaining object similarity judgments
Recent advances in Deep convolutional Neural Networks (DNNs) have enabled
unprecedentedly accurate computational models of brain representations, and present an …
unprecedentedly accurate computational models of brain representations, and present an …
Deep neural networks predict hierarchical spatio-temporal cortical dynamics of human visual object recognition
The complex multi-stage architecture of cortical visual pathways provides the neural basis
for efficient visual object recognition in humans. However, the stage-wise computations …
for efficient visual object recognition in humans. However, the stage-wise computations …
Joint representation of color and form in convolutional neural networks: A stimulus-rich network perspective
To interact with real-world objects, any effective visual system must jointly code the unique
features defining each object. Despite decades of neuroscience research, we still lack a firm …
features defining each object. Despite decades of neuroscience research, we still lack a firm …
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
Primates, including humans, can typically recognize objects in visual images at a glance
despite naturally occurring identity-preserving image transformations (eg, changes in …
despite naturally occurring identity-preserving image transformations (eg, changes in …