Using deep learning to probe the neural code for images in primary visual cortex
暂无分享,去创建一个
[1] D. Hubel,et al. Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.
[2] R. Desimone,et al. Predicting responses of nonlinear neurons in monkey striate cortex to complex patterns , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[3] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[4] Brian Lau,et al. Computational subunits of visual cortical neurons revealed by artificial neural networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[5] J. Gallant,et al. Natural Stimulus Statistics Alter the Receptive Field Structure of V1 Neurons , 2004, The Journal of Neuroscience.
[6] Ryan J. Prenger,et al. Nonlinear V1 responses to natural scenes revealed by neural network analysis , 2004, Neural Networks.
[7] David J. Field,et al. How Close Are We to Understanding V1? , 2005, Neural Computation.
[8] Ryan J. Prenger,et al. The Berkeley Wavelet Transform: A Biologically Inspired Orthogonal Wavelet Transform , 2008, Neural Computation.
[9] B. Willmore,et al. Neural Representation of Natural Images in Visual Area V2 , 2010, The Journal of Neuroscience.
[10] Michael Robert DeWeese,et al. A Sparse Coding Model with Synaptically Local Plasticity and Spiking Neurons Can Account for the Diverse Shapes of V1 Simple Cell Receptive Fields , 2011, PLoS Comput. Biol..
[11] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[12] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[13] Michael Robert DeWeese,et al. Sparse Coding Models Can Exhibit Decreasing Sparseness while Learning Sparse Codes for Natural Images , 2013, PLoS Comput. Biol..
[14] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[15] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[16] Mark Mazurek,et al. Robust quantification of orientation selectivity and direction selectivity , 2014, Front. Neural Circuits.
[17] Andrea Vedaldi,et al. Understanding deep image representations by inverting them , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] O. Schwartz,et al. Flexible Gating of Contextual Influences in Natural Vision , 2015, Nature Neuroscience.
[19] Robert H. Cormack,et al. Miniaturized fiber-coupled confocal fluorescence microscope with an electrowetting variable focus lens using no moving parts. , 2015, Optics letters.
[20] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[21] Eero P. Simoncelli,et al. A Convolutional Subunit Model for Neuronal Responses in Macaque V1 , 2015, The Journal of Neuroscience.
[22] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[23] Cyriel M A Pennartz,et al. Population-Level Neural Codes Are Robust to Single-Neuron Variability from a Multidimensional Coding Perspective. , 2016, Cell reports.
[24] Surya Ganguli,et al. Deep Learning Models of the Retinal Response to Natural Scenes , 2017, NIPS.
[25] Eero P. Simoncelli,et al. Selectivity and tolerance for visual texture in macaque V2 , 2016, Proceedings of the National Academy of Sciences.
[26] J. DiCarlo,et al. Using goal-driven deep learning models to understand sensory cortex , 2016, Nature Neuroscience.
[27] Andrew J. King,et al. Measuring the Performance of Neural Models , 2016, Front. Comput. Neurosci..
[28] Leon A. Gatys,et al. Deep convolutional models improve predictions of macaque V1 responses to natural images , 2019, PLoS Comput. Biol..