Using goal-driven deep learning models to understand sensory cortex
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[1] D. Hubel,et al. Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.
[2] J. Movshon,et al. Spatial summation in the receptive fields of simple cells in the cat's striate cortex. , 1978, The Journal of physiology.
[3] E. Yund,et al. Responses of striate cortex cells to grating and checkerboard patterns. , 1979, The Journal of physiology.
[4] H. Barlow. Vision: A computational investigation into the human representation and processing of visual information: David Marr. San Francisco: W. H. Freeman, 1982. pp. xvi + 397 , 1983 .
[5] S. Ullman. Visual routines , 1984, Cognition.
[6] G. Hoyle. The scope of neuroethology , 1984, Behavioral and Brain Sciences.
[7] J. P. Jones,et al. An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.
[8] D. J. Felleman,et al. Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.
[9] Geoffrey E. Hinton,et al. The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.
[10] N. Logothetis,et al. Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.
[11] W Singer,et al. Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.
[12] D. C. Essen,et al. Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey. , 1996, Journal of neurophysiology.
[13] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[14] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[15] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[16] A. Zador,et al. Neural representation and the cortical code. , 2000, Annual review of neuroscience.
[17] Bruno A. Olshausen,et al. Learning Sparse Image Codes using a Wavelet Pyramid Architecture , 2000, NIPS.
[18] David J. Freedman,et al. Categorical representation of visual stimuli in the primate prefrontal cortex. , 2001, Science.
[19] C. Connor,et al. Population coding of shape in area V4 , 2002, Nature Neuroscience.
[20] R. Malach,et al. The topography of high-order human object areas , 2002, Trends in Cognitive Sciences.
[21] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.
[22] Nicole C. Rust,et al. Do We Know What the Early Visual System Does? , 2005, The Journal of Neuroscience.
[23] Jonathan Z. Simon,et al. Stimulus-invariant processing and spectrotemporal reverse correlation in primary auditory cortex , 2005, Journal of Computational Neuroscience.
[24] Tomaso Poggio,et al. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.
[25] Eero P. Simoncelli,et al. How MT cells analyze the motion of visual patterns , 2006, Nature Neuroscience.
[26] J. Gallant,et al. Spectral receptive field properties explain shape selectivity in area V4. , 2006, Journal of neurophysiology.
[27] Thomas Serre,et al. A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.
[28] G. Griffin,et al. Caltech-256 Object Category Dataset , 2007 .
[29] Tomaso Poggio,et al. Trade-Off between Object Selectivity and Tolerance in Monkey Inferotemporal Cortex , 2007, The Journal of Neuroscience.
[30] David D. Cox,et al. Opinion TRENDS in Cognitive Sciences Vol.11 No.8 Untangling invariant object recognition , 2022 .
[31] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[32] Eric T. Carlson,et al. A neural code for three-dimensional object shape in macaque inferotemporal cortex , 2008, Nature Neuroscience.
[33] Eero P. Simoncelli,et al. Spatio-temporal correlations and visual signalling in a complete neuronal population , 2008, Nature.
[34] Nicolas Pinto,et al. Why is Real-World Visual Object Recognition Hard? , 2008, PLoS Comput. Biol..
[35] Long Zhu,et al. Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion , 2008, ECCV.
[36] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[37] Nikolaus Kriegeskorte,et al. Relating Population-Code Representations between Man, Monkey, and Computational Models , 2009, Front. Neurosci..
[38] Peter Norvig,et al. The Unreasonable Effectiveness of Data , 2009, IEEE Intelligent Systems.
[39] David D. Cox,et al. A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation , 2009, PLoS Comput. Biol..
[40] Nicole C. Rust,et al. Selectivity and Tolerance (“Invariance”) Both Increase as Visual Information Propagates from Cortical Area V4 to IT , 2010, The Journal of Neuroscience.
[41] Léon Bottou,et al. Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.
[42] Eero P. Simoncelli,et al. Metamers of the ventral stream , 2011, Nature Neuroscience.
[43] M. Carandini,et al. Normalization as a canonical neural computation , 2011, Nature Reviews Neuroscience.
[44] H. B. Barlow,et al. Possible Principles Underlying the Transformations of Sensory Messages , 2012 .
[45] Kwabena Boahen,et al. Silicon Neurons That Compute , 2012, ICANN.
[46] Jasper Snoek,et al. Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.
[47] James J. DiCarlo,et al. How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.
[48] Yoshua Bengio,et al. Deep Learning of Representations for Unsupervised and Transfer Learning , 2011, ICML Unsupervised and Transfer Learning.
[49] Christopher D. Harvey,et al. Choice-specific sequences in parietal cortex during a virtual-navigation decision task , 2012, Nature.
[50] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[51] J. Reynolds,et al. Trade-off between curvature tuning and position invariance in visual area V4 , 2013, Proceedings of the National Academy of Sciences.
[52] David D. Cox,et al. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures , 2013, ICML.
[53] Nicole C. Rust,et al. Signals in inferotemporal and perirhinal cortex suggest an “untangling” of visual target information , 2013, Nature Neuroscience.
[54] W. Newsome,et al. Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.
[55] Ha Hong,et al. Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream , 2013, NIPS.
[56] C. Connor,et al. Curvature processing dynamics in macaque area V4. , 2013, Cerebral cortex.
[57] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[58] Stefan Carlsson,et al. CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[59] Daniel L. K. Yamins,et al. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition , 2014, PLoS Comput. Biol..
[60] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[61] Nikolaus Kriegeskorte,et al. Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation , 2014, PLoS Comput. Biol..
[62] Ha Hong,et al. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance , 2015, The Journal of Neuroscience.
[63] Thomas S. Huang,et al. Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition? , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).
[64] Sergey Levine,et al. Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models , 2015, ArXiv.
[65] Eve Marder,et al. Understanding Brains: Details, Intuition, and Big Data , 2015, PLoS biology.
[66] K. Norman,et al. Neural Differentiation Tracks Improved Recall of Competing Memories Following Interleaved Study and Retrieval Practice. , 2015, Cerebral cortex.
[67] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[68] J. DiCarlo,et al. Optogenetic and pharmacological suppression of spatial clusters of face neurons reveal their causal role in face gender discrimination , 2015, Proceedings of the National Academy of Sciences.
[69] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[70] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[71] Marcel A. J. van Gerven,et al. Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream , 2014, The Journal of Neuroscience.
[72] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[73] Leon A. Gatys,et al. A Neural Algorithm of Artistic Style , 2015, ArXiv.