Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision

Recurrence is required to capture the representational dynamics of the human visual system

(2019) Tim C. Kietzmann et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks

(2018) Rishi Rajalingham et al.   JOURNAL OF NEUROSCIENCE

Recurrent computations for visual pattern completion

(2018) Hanlin Tang et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Representational Dynamics of Facial Viewpoint Encoding

(2017) Tim C. Kietzmann et al.   JOURNAL OF COGNITIVE NEUROSCIENCE

Using goal-driven deep learning models to understand sensory cortex

(2016) Daniel L K Yamins et al.   NATURE NEUROSCIENCE

Extensive training leads to temporal and spatial shifts of cortical activity underlying visual category selectivity

(2016) Tim C. Kietzmann et al.   NEUROIMAGE

ImageNet Large Scale Visual Recognition Challenge

(2015) Olga Russakovsky et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

Comparison of Object Recognition Behavior in Human and Monkey

(2015) R. Rajalingham et al.   JOURNAL OF NEUROSCIENCE

Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream

(2015) U. Guclu et al.   JOURNAL OF NEUROSCIENCE

Emerging Object Representations in the Visual System Predict Reaction Times for Categorization

(2015) J. Brendan Ritchie et al.   PLoS Computational Biology

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing

(2015) Nikolaus Kriegeskorte   Annual Review of Vision Science

Predicting the Time Course of Individual Objects with MEG

(2014) Alex Clarke et al.   CEREBRAL CORTEX

Performance-optimized hierarchical models predict neural responses in higher visual cortex

(2014) D. L. K. Yamins et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A Toolbox for Representational Similarity Analysis

(2014) Hamed Nili et al.   PLoS Computational Biology

Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition

(2014) Charles F. Cadieu et al.   PLoS Computational Biology

Reaction Time for Object Categorization Is Predicted by Representational Distance

(2013) Thomas A. Carlson et al.   JOURNAL OF COGNITIVE NEUROSCIENCE

Anatomy of hierarchy: Feedforward and feedback pathways in macaque visual cortex

(2013) Nikola T. Markov et al.   JOURNAL OF COMPARATIVE NEUROLOGY

How Does the Brain Solve Visual Object Recognition?

(2012) James J. DiCarlo et al.   NEURON

Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions

(2011) N. Halko et al.   SIAM REVIEW

Functional Compartmentalization and Viewpoint Generalization Within the Macaque Face-Processing System

(2010) W. A. Freiwald et al.   SCIENCE

Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey

(2008) Nikolaus Kriegeskorte et al.   NEURON