Hierarchical temporal prediction captures motion processing from retina to higher visual cortex
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Andrew J. King | Nicol S. Harper | Ben D. B. Willmore | Yosef Singer | B. Willmore | A. King | N. Harper | Y. Singer | A. King
[1] O. Marre,et al. Toward a unified theory of efficient, predictive, and sparse coding , 2017, Proceedings of the National Academy of Sciences.
[2] Stephanie E. Palmer,et al. Optimal Prediction in the Retina and Natural Motion Statistics , 2016 .
[3] Terrence J. Sejnowski,et al. The “independent components” of natural scenes are edge filters , 1997, Vision Research.
[4] D. Hubel,et al. Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.
[5] D. Ruderman,et al. Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[6] Andrew J King,et al. Sensory cortex is optimized for prediction of future input , 2017, bioRxiv.
[7] A. U.S.,et al. Predictability , Complexity , and Learning , 2002 .
[8] Charles G. Gross,et al. Pattern recognition mechanisms , 1985 .
[9] Konrad P. Körding,et al. Extracting Slow Subspaces from Natural Videos Leads to Complex Cells , 2001, ICANN.
[10] Eero P. Simoncelli,et al. A model of neuronal responses in visual area MT , 1998, Vision Research.
[11] Marcin J. Skwark,et al. Improving Contact Prediction along Three Dimensions , 2014, PLoS Comput. Biol..
[12] R. Shapley,et al. Cat and monkey retinal ganglion cells and their visual functional roles , 1986, Trends in Neurosciences.
[13] Laurenz Wiskott,et al. Slow feature analysis yields a rich repertoire of complex cell properties. , 2005, Journal of vision.
[14] D H HUBEL,et al. RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT. , 1965, Journal of neurophysiology.
[15] Matthias Bethge,et al. Slowness and Sparseness Have Diverging Effects on Complex Cell Learning , 2014, PLoS Comput. Biol..
[16] E. Adelson,et al. The analysis of moving visual patterns , 1985 .
[17] A. B. Bonds,et al. Classifying simple and complex cells on the basis of response modulation , 1991, Vision Research.
[18] Margaret S Livingstone,et al. End-Stopping and the Aperture Problem Two-Dimensional Motion Signals in Macaque V1 , 2003, Neuron.
[19] Richard E. Turner,et al. A Structured Model of Video Reproduces Primary Visual Cortical Organisation , 2009, PLoS Comput. Biol..
[20] Simon Osindero,et al. Modelling the Statistics of Natural Images with Topographic Product of Student-t Models , 2004 .
[21] 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.
[22] Konrad P. Körding,et al. Learning the Nonlinearity of Neurons from Natural Visual Stimuli , 2003, Neural Computation.
[23] Ralph D Freeman,et al. Direction selectivity of neurons in the visual cortex is non‐linear and lamina‐dependent , 2016, The European journal of neuroscience.
[24] E J Chichilnisky,et al. A simple white noise analysis of neuronal light responses , 2001, Network.
[25] H. B. Barlow,et al. Possible Principles Underlying the Transformations of Sensory Messages , 2012 .
[26] Konrad Paul Kording,et al. How are complex cell properties adapted to the statistics of natural stimuli? , 2004, Journal of neurophysiology.
[27] J. Movshon,et al. Spatial summation in the receptive fields of simple cells in the cat's striate cortex. , 1978, The Journal of physiology.
[28] Steven C Dakin,et al. An oblique effect for local motion: psychophysics and natural movie statistics. , 2005, Journal of vision.
[29] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[30] Eero P. Simoncelli,et al. How MT cells analyze the motion of visual patterns , 2006, Nature Neuroscience.
[31] Surya Ganguli,et al. The emergence of multiple retinal cell types through efficient coding of natural movies , 2018, bioRxiv.
[32] A. Borst. Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.
[33] J. V. van Hateren,et al. Independent component filters of natural images compared with simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[34] Eero P. Simoncelli,et al. Spatiotemporal Elements of Macaque V1 Receptive Fields , 2005, Neuron.
[35] Bruno A. Olshausen,et al. Sparse Coding Of Time-Varying Natural Images , 2010 .
[36] S. Zeki. Functional organization of a visual area in the posterior bank of the superior temporal sulcus of the rhesus monkey , 1974, The Journal of physiology.
[37] Terrence J. Sejnowski,et al. Multi-state Modeling of Biomolecules , 2014, PLoS Comput. Biol..
[38] 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 .
[39] F. Jäkel,et al. Spatial four-alternative forced-choice method is the preferred psychophysical method for naïve observers. , 2006, Journal of vision.
[40] James C. R. Whittington,et al. Theories of Error Back-Propagation in the Brain , 2019, Trends in Cognitive Sciences.
[41] E. Bizzi,et al. The Cognitive Neurosciences , 1996 .
[42] Rajesh P. N. Rao,et al. Predictive Coding , 2019, A Blueprint for the Hard Problem of Consciousness.
[43] J. Movshon,et al. Dynamics of motion signaling by neurons in macaque area MT , 2005, Nature Neuroscience.
[44] Hassana K. Oyibo,et al. Experience-dependent spatial expectations in mouse visual cortex , 2016, Nature Neuroscience.
[45] Nicholas J. Priebe,et al. Emergence of Orientation Selectivity in the Mammalian Visual Pathway , 2013, The Journal of Neuroscience.
[46] Aapo Hyvärinen,et al. A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images , 2001, Vision Research.
[47] Bruno A. Olshausen,et al. Learning Intermediate-Level Representations of Form and Motion from Natural Movies , 2012, Neural Computation.
[48] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[49] Aapo Hyvärinen,et al. Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video , 2003, Neural Computation.
[50] Aapo Hyvärinen,et al. Bubbles: a unifying framework for low-level statistical properties of natural image sequences. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.
[51] R. Shapley,et al. Orientation Selectivity in Macaque V1: Diversity and Laminar Dependence , 2002, The Journal of Neuroscience.
[52] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[53] Michael S. Lewicki,et al. Emergence of complex cell properties by learning to generalize in natural scenes , 2009, Nature.
[54] S. Laughlin,et al. Predictive coding: a fresh view of inhibition in the retina , 1982, Proceedings of the Royal Society of London. Series B. Biological Sciences.
[55] J. DiCarlo,et al. Using goal-driven deep learning models to understand sensory cortex , 2016, Nature Neuroscience.
[56] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[57] S. W. Kuffler. Discharge patterns and functional organization of mammalian retina. , 1953, Journal of neurophysiology.
[58] Aapo Hyvärinen,et al. Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces , 2000, Neural Computation.
[59] D. Hubel,et al. Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.
[60] Felix Creutzig,et al. Predictive Coding and the Slowness Principle: An Information-Theoretic Approach , 2008, Neural Computation.
[61] Geoffrey E. Hinton,et al. Topographic Product Models Applied to Natural Scene Statistics , 2006, Neural Computation.
[62] H. Barlow. Summation and inhibition in the frog's retina , 1953, The Journal of physiology.
[63] Anthony J. Movshon,et al. Visual Response Properties of Striate Cortical Neurons Projecting to Area MT in Macaque Monkeys , 1996, The Journal of Neuroscience.
[64] J. Movshon,et al. Receptive field organization of complex cells in the cat's striate cortex. , 1978, The Journal of physiology.
[65] Bruno A. Olshausen,et al. Learning sparse, overcomplete representations of time-varying natural images , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).
[66] M. Feller,et al. Mechanisms underlying development of visual maps and receptive fields. , 2008, Annual review of neuroscience.
[67] Anthony M. Norcia,et al. Neural correlates of shape-from-shading , 2002 .
[68] Lynne Kiorpes,et al. Visual development in primates: Neural mechanisms and critical periods , 2015, Developmental neurobiology.