Deep Neural Networks in Computational Neuroscience

Deep Residual Learning for Image Recognition

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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ImageNet classification with deep convolutional neural networks

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Long Short-Term Memory

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Understanding Neural Networks Through Deep VisualizationPreprint

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Slow Feature Analysis: Unsupervised Learning of Invariances

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Spectral fingerprints of large-scale neuronal interactions

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Deep supervised, but not unsupervised, models may explain IT cortical representation

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Population receptive field estimates in human visual cortex

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Predicting Human Brain Activity Associated with the Meanings of Nouns

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Deep neural networks: a new framework for modeling biological vision and brain information processing

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Parenting Stress, Mental Health, Dyadic Adjustment: A Structural Equation Model

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Recurrent convolutional neural network for object recognition

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A Large-Scale Model of the Functioning Brain

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Random synaptic feedback weights support error backpropagation for deep learning

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Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream

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Deeply learned face representations are sparse, selective, and robust

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Encoding and decoding in fMRI

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A Continuous Semantic Space Describes the Representation of Thousands of Object and Action Categories across the Human Brain

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A Toolbox for Representational Similarity Analysis

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Representational geometry: integrating cognition, computation, and the brain

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Resolving human object recognition in space and time

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

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Center–periphery organization of human object areas

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Comparing face patch systems in macaques and humans

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Statistically optimal perception and learning: from behavior to neural representations

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Metamers of the ventral stream

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Learning invariance from transformation sequences

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Genetic evidence that Nkx2.2 acts primarily downstream of Neurog3 in pancreatic endocrine lineage development

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INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM

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Bayesian Reconstruction of Natural Images from Human Brain Activity

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Deep residual learning for image steganalysis

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On simplicity and complexity in the brave new world of large-scale neuroscience

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Towards deep learning with segregated dendrites

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How Close Are We to Understanding V1?

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On the Use of Human Mobility Proxies for Modeling Epidemics

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Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks

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Interpreting Deep Visual Representations via Network Dissection

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Deep Neural Networks as a Computational Model for Human Shape Sensitivity

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Slow feature analysis yields a rich repertoire of complex cell properties

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An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity

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Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex

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Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual CortexPreprint

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Difference Target Propagation

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Deep Learning Models of the Retinal Response to Natural Scenes

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Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance

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Equivalence of Backpropagation and Contrastive Hebbian Learning in a Layered Network

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Measuring Integrated Information from the Decoding Perspective

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Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

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Human Object-Similarity Judgments Reflect and Transcend the Primate-IT Object Representation

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Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet

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Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition

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Functional and Topological Conditions for Explosive Synchronization Develop in Human Brain Networks with the Onset of Anesthetic-Induced Unconsciousness

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Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex

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Early recurrent feedback facilitates visual object recognition under challenging conditions

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Dynamic Computational Time for Visual Attention

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The Limits of Feedforward Vision: Recurrent Processing Promotes Robust Object Recognition when Objects Are Degraded

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Toward an integration of deep learning and neuroscience

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