Learning divisive normalization in primary visual cortex
暂无分享,去创建一个
Alexander S. Ecker | Matthias Bethge | Andreas S. Tolias | George H. Denfield | Edgar Y. Walker | Santiago A. Cadena | Max F. Burg
[1] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[2] A. Angelucci,et al. Contribution of feedforward, lateral and feedback connections to the classical receptive field center and extra-classical receptive field surround of primate V1 neurons. , 2006, Progress in brain research.
[3] Eero P. Simoncelli,et al. To appear in: The New Cognitive Neurosciences, 3rd edition Editor: M. Gazzaniga. MIT Press, 2004. Characterization of Neural Responses with Stochastic Stimuli , 2022 .
[4] Liam Paninski,et al. Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses , 2016, ICLR.
[5] O. Schwartz,et al. Flexible Gating of Contextual Influences in Natural Vision , 2015, Nature Neuroscience.
[6] James J DiCarlo,et al. Multiple Object Response Normalization in Monkey Inferotemporal Cortex , 2005, The Journal of Neuroscience.
[7] Surya Ganguli,et al. Deep Learning Models of the Retinal Response to Natural Scenes , 2017, NIPS.
[8] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[9] Eero P. Simoncelli,et al. Natural signal statistics and sensory gain control , 2001, Nature Neuroscience.
[10] J. Movshon,et al. Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex , 1997, The Journal of Neuroscience.
[11] Rufin Vogels,et al. Divisive Normalization Predicts Adaptation-Induced Response Changes in Macaque Inferior Temporal Cortex , 2016, The Journal of Neuroscience.
[12] Nikolaus Kriegeskorte,et al. Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation , 2014, PLoS Comput. Biol..
[13] C. Blakemore,et al. Lateral inhibition between orientation detectors in the cat's visual cortex , 2004, Experimental Brain Research.
[14] Nicole C. Rust,et al. Do We Know What the Early Visual System Does? , 2005, The Journal of Neuroscience.
[15] Petr Znamenskiy,et al. Functional selectivity and specific connectivity of inhibitory neurons in primary visual cortex , 2018, bioRxiv.
[16] J. Movshon,et al. Nature and interaction of signals from the receptive field center and surround in macaque V1 neurons. , 2002, Journal of neurophysiology.
[17] J. Touryan,et al. Spatial Structure of Complex Cell Receptive Fields Measured with Natural Images , 2005, Neuron.
[18] et al.,et al. Jupyter Notebooks - a publishing format for reproducible computational workflows , 2016, ELPUB.
[19] D. Heeger. Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.
[20] Alexander S. Ecker,et al. Population code in mouse V1 facilitates read-out of natural scenes through increased sparseness , 2014, Nature Neuroscience.
[21] Gaël Varoquaux,et al. The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.
[22] Hongkui Zeng,et al. Differential tuning and population dynamics of excitatory and inhibitory neurons reflect differences in local intracortical connectivity , 2011, Nature Neuroscience.
[23] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[24] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[25] D. Burr,et al. Functional implications of cross-orientation inhibition of cortical visual cells. I. Neurophysiological evidence , 1982, Proceedings of the Royal Society of London. Series B. Biological Sciences.
[26] Leon A. Gatys,et al. Deep convolutional models improve predictions of macaque V1 responses to natural images , 2017, bioRxiv.
[27] Johannes Burge,et al. The statistics of how natural images drive the responses of neurons , 2019, Journal of vision.
[28] M. Carandini,et al. Normalization as a canonical neural computation , 2011, Nature Reviews Neuroscience.
[29] Wes McKinney,et al. Data Structures for Statistical Computing in Python , 2010, SciPy.
[30] K. H. Britten,et al. Contrast dependence of response normalization in area MT of the rhesus macaque. , 2002, Journal of neurophysiology.
[31] C Koch,et al. Revisiting spatial vision: toward a unifying model. , 2000, Journal of the Optical Society of America. A, Optics, image science, and vision.
[32] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[33] H. B. Barlow,et al. Possible Principles Underlying the Transformations of Sensory Messages , 2012 .
[34] Valero Laparra,et al. End-to-end Optimized Image Compression , 2016, ICLR.
[35] Alexander S. Ecker,et al. Attentional fluctuations induce shared variability in macaque primary visual cortex , 2017, Nature Communications.
[36] R. Desimone,et al. Interacting Roles of Attention and Visual Salience in V4 , 2003, Neuron.
[37] J. Movshon,et al. Selectivity and spatial distribution of signals from the receptive field surround in macaque V1 neurons. , 2002, Journal of neurophysiology.
[38] Christopher J. Rozell,et al. Visual Nonclassical Receptive Field Effects Emerge from Sparse Coding in a Dynamical System , 2013, PLoS Comput. Biol..
[39] Matthias Bethge,et al. The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction , 2008, NIPS.
[40] J L Gallant,et al. Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.
[41] Sepp Hochreiter,et al. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.
[42] Andreas S. Tolias,et al. DataJoint: A Simpler Relational Data Model , 2018, ArXiv.
[43] I. Ohzawa,et al. Organization of suppression in receptive fields of neurons in cat visual cortex. , 1992, Journal of neurophysiology.
[44] I. Ohzawa,et al. Length and width tuning of neurons in the cat's primary visual cortex. , 1994, Journal of neurophysiology.
[45] Ming Li,et al. Convolutional neural network models of V1 responses to complex patterns , 2018, Journal of Computational Neuroscience.
[46] Alexander S. Ecker,et al. Inception loops discover what excites neurons most using deep predictive models , 2019, Nature Neuroscience.
[47] Eero P. Simoncelli,et al. A Convolutional Subunit Model for Neuronal Responses in Macaque V1 , 2015, The Journal of Neuroscience.
[48] M. Carandini,et al. Suppression without Inhibition in Visual Cortex , 2002, Neuron.
[49] Dirk Merkel,et al. Docker: lightweight Linux containers for consistent development and deployment , 2014 .
[50] E H Adelson,et al. Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.
[51] Alexander S. Ecker,et al. DataJoint: managing big scientific data using MATLAB or Python , 2015, bioRxiv.
[52] Eero P. Simoncelli,et al. Natural image statistics and divisive normalization: Modeling nonlinearity and adaptation in cortical neurons , 2002 .
[53] Yuwei Cui,et al. Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs , 2013, PLoS Comput. Biol..
[54] James A. Bednar,et al. Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes , 2016, PLoS Comput. Biol..
[55] Alexander S. Ecker,et al. Stimulus domain transfer in recurrent models for large scale cortical population prediction on video , 2018, NeurIPS.
[56] Eero P. Simoncelli,et al. Spatiotemporal Elements of Macaque V1 Receptive Fields , 2005, Neuron.
[57] Alexander S. Ecker,et al. Neural system identification for large populations separating "what" and "where" , 2017, NIPS.
[58] J. Movshon,et al. Time Course and Time-Distance Relationships for Surround Suppression in Macaque V1 Neurons , 2003, The Journal of Neuroscience.
[59] Elijah D. Christensen,et al. Using deep learning to probe the neural code for images in primary visual cortex , 2019, Journal of vision.
[60] A. B. Bonds. Role of Inhibition in the Specification of Orientation Selectivity of Cells in the Cat Striate Cortex , 1989, Visual Neuroscience.
[61] L. Paninski,et al. Temporal Precision in the Visual Pathway through the Interplay of Excitation and Stimulus- Driven Suppression , 2022 .
[62] Alex R. Wade,et al. Representation of Concurrent Stimuli by Population Activity in Visual Cortex , 2009, Neuron.
[63] Ryan J. Prenger,et al. The Berkeley Wavelet Transform: A Biologically Inspired Orthogonal Wavelet Transform , 2008, Neural Computation.
[64] Selmaan N. Chettih,et al. Single-neuron perturbations reveal feature-specific competition in V1 , 2019, Nature.
[65] Heiko H Schütt,et al. An image-computable psychophysical spatial vision model. , 2017, Journal of vision.
[66] John D. Hunter,et al. Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.
[67] A. Petrov,et al. The Divisive-Normalization Model of V1 Neurons: A Comprehensive Comparison of Physiological Data and Model Predictions , 2016, bioRxiv.
[68] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[69] Joel Zylberberg,et al. Inhibitory Interneurons Decorrelate Excitatory Cells to Drive Sparse Code Formation in a Spiking Model of V1 , 2013, The Journal of Neuroscience.