Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity

Unsupervised neural network models of the ventral visual stream

(2021) Chengxu Zhuang et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Deep convolutional models improve predictions of macaque V1 responses to natural images

(2019) Santiago A. Cadena et al.   PLoS Computational Biology

High-dimensional geometry of population responses in visual cortex

(2019) Carsen Stringer et al.   NATURE

Understanding the development of amblyopia using macaque monkey models

(2019) Lynne Kiorpes   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Potential downside of high initial visual acuity

(2018) Lukas Vogelsang et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Convolutional neural network-based encoding and decoding of visual object recognition in space and time

(2017) K. Seeliger et al.   NEUROIMAGE

Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

(2016) Radoslaw Martin Cichy et al.   Scientific Reports

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

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

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

Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation

(2014) Seyed-Mahdi Khaligh-Razavi et al.   PLoS Computational Biology