Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision

Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models

(2017) Seyed-Mahdi Khaligh-Razavi et al.   JOURNAL OF MATHEMATICAL PSYCHOLOGY

Seeing it all: Convolutional network layers map the function of the human visual system

(2017) Michael Eickenberg et al.   NEUROIMAGE

Modeling the Dynamics of Human Brain Activity with Recurrent Neural Networks

(2017) Umut Güçlü et al.   Frontiers in Computational Neuroscience

Generic decoding of seen and imagined objects using hierarchical visual features

(2017) Tomoyasu Horikawa et al.   Nature Communications

Mastering the game of Go with deep neural networks and tree search

(2016) David Silver et al.   NATURE

A multi-modal parcellation of human cerebral cortex

(2016) Matthew F. Glasser et al.   NATURE

Using goal-driven deep learning models to understand sensory cortex

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

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

Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal

(2015) Haiguang Wen et al.   BRAIN TOPOGRAPHY

ImageNet Large Scale Visual Recognition Challenge

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

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

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

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

Deep learning

(2015) Yann LeCun et al.   NATURE

Hierarchical process memory: memory as an integral component of information processing

(2015) Uri Hasson et al.   TRENDS IN COGNITIVE SCIENCES

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

(2015) Nikolaus Kriegeskorte   Annual Review of Vision Science

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

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

Increasing the reliability of data analysis of functional magnetic resonance imaging by applying a new blockwise permutation method

(2014) Daniela Adolf et al.   Frontiers in Neuroinformatics

Compressive spatial summation in human visual cortex

(2013) Kendrick N. Kay et al.   JOURNAL OF NEUROPHYSIOLOGY

The minimal preprocessing pipelines for the Human Connectome Project

(2013) Matthew F. Glasser et al.   NEUROIMAGE

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

(2012) Geoffrey Hinton et al.   IEEE SIGNAL PROCESSING MAGAZINE

The Neural Selection and Integration of Actions and Objects: An fMRI Study

(2012) Eun Young Yoon et al.   JOURNAL OF COGNITIVE NEUROSCIENCE

Slow Cortical Dynamics and the Accumulation of Information over Long Timescales

(2012) Christopher J. Honey et al.   NEURON

The free-energy principle: a unified brain theory?

(2010) Karl Friston   NATURE REVIEWS NEUROSCIENCE

Encoding and decoding in fMRI

(2010) Thomas Naselaris et al.   NEUROIMAGE

State-dependent computations: spatiotemporal processing in cortical networks

(2009) Dean V. Buonomano et al.   NATURE REVIEWS NEUROSCIENCE

Power-Law Scaling in the Brain Surface Electric Potential

(2009) Kai J. Miller et al.   PLoS Computational Biology

A Hierarchy of Temporal Receptive Windows in Human Cortex

(2008) U. Hasson et al.   JOURNAL OF NEUROSCIENCE