Modeling Inhibitory Interneurons in Efficient Sensory Coding Models
暂无分享,去创建一个
[1] S. Laughlin,et al. Energy limitation as a selective pressure on the evolution of sensory systems , 2008, Journal of Experimental Biology.
[2] Christian K. Machens,et al. Predictive Coding of Dynamical Variables in Balanced Spiking Networks , 2013, PLoS Comput. Biol..
[3] Christopher J. Rozell,et al. Visual Nonclassical Receptive Field Effects Emerge from Sparse Coding in a Dynamical System , 2013, PLoS Comput. Biol..
[4] Christopher J. Rozell,et al. Low Power Sparse Approximation on Reconfigurable Analog Hardware , 2012, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.
[5] Pablo A. Parrilo,et al. Rank-Sparsity Incoherence for Matrix Decomposition , 2009, SIAM J. Optim..
[6] Tao Hu,et al. A Network of Spiking Neurons for Computing Sparse Representations in an Energy-Efficient Way , 2012, Neural Computation.
[7] Li I. Zhang,et al. Visual Representations by Cortical Somatostatin Inhibitory Neurons—Selective But with Weak and Delayed Responses , 2010, The Journal of Neuroscience.
[8] L. Martinez,et al. Circuits that build visual cortical receptive fields , 2006, Trends in Neurosciences.
[9] J L Gallant,et al. Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.
[10] M. Scanziani,et al. How Inhibition Shapes Cortical Activity , 2011, Neuron.
[11] Christopher J. Rozell,et al. Short-Term Memory Capacity in Networks via the Restricted Isometry Property , 2013, Neural Computation.
[12] Eero P. Simoncelli. Vision and the statistics of the visual environment , 2003, Current Opinion in Neurobiology.
[13] M. Scanziani,et al. Inhibition of Inhibition in Visual Cortex: The Logic of Connections Between Molecularly Distinct Interneurons , 2013, Nature Neuroscience.
[14] Gilad Silberberg,et al. Polysynaptic subcircuits in the neocortex: spatial and temporal diversity , 2008, Current Opinion in Neurobiology.
[15] Christopher J. Rozell,et al. A Common Network Architecture Efficiently Implements a Variety of Sparsity-Based Inference Problems , 2012, Neural Computation.
[16] Eero P. Simoncelli,et al. Natural signal statistics and sensory gain control , 2001, Nature Neuroscience.
[17] Arno C. Schmitt,et al. Inhibitory interneurons in a cortical column form hot zones of inhibition in layers 2 and 5A , 2011, Proceedings of the National Academy of Sciences.
[18] Nathan R. Wilson,et al. Response Features of Parvalbumin-Expressing Interneurons Suggest Precise Roles for Subtypes of Inhibition in Visual Cortex , 2010, Neuron.
[19] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[20] Cynthia Sámano,et al. Neurotransmitter segregation: Functional and plastic implications , 2012, Progress in Neurobiology.
[21] Christopher J. Rozell,et al. Configurable hardware integrate and fire neurons for sparse approximation , 2013, Neural Networks.
[22] N. Stanietsky,et al. The interaction of TIGIT with PVR and PVRL2 inhibits human NK cell cytotoxicity , 2009, Proceedings of the National Academy of Sciences.
[23] Richard G. Baraniuk,et al. Sparse Coding via Thresholding and Local Competition in Neural Circuits , 2008, Neural Computation.
[24] Matthew R. Krause,et al. Synaptic and Network Mechanisms of Sparse and Reliable Visual Cortical Activity during Nonclassical Receptive Field Stimulation , 2010, Neuron.
[25] Yi Ma,et al. The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices , 2010, Journal of structural biology.
[26] Stephen P. Boyd,et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..
[27] M. Larkum. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex , 2013, Trends in Neurosciences.
[28] P. J. Sjöström,et al. Functional specificity of local synaptic connections in neocortical networks , 2011, Nature.
[29] Rajesh P. N. Rao,et al. Probabilistic Models of the Brain: Perception and Neural Function , 2002 .
[30] Sen Song,et al. Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits , 2005, PLoS biology.
[31] Justin K. Romberg,et al. Convergence and Rate Analysis of Neural Networks for Sparse Approximation , 2011, IEEE Transactions on Neural Networks and Learning Systems.
[32] Justin K. Romberg,et al. Convergence of a neural network for sparse approximation using the nonsmooth Łojasiewicz inequality , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[33] Kim Steenstrup Pedersen,et al. The Nonlinear Statistics of High-Contrast Patches in Natural Images , 2003, International Journal of Computer Vision.
[34] Martin Rehn,et al. A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields , 2007, Journal of Computational Neuroscience.
[35] Anders Krogh,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[36] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[37] Bruno A Olshausen,et al. Sparse coding of sensory inputs , 2004, Current Opinion in Neurobiology.
[38] A. Borst. Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.
[39] 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.
[40] Nathan R. Wilson,et al. Division and subtraction by distinct cortical inhibitory networks in vivo , 2012, Nature.
[41] Arthur W. Wetzel,et al. Network anatomy and in vivo physiology of visual cortical neurons , 2011, Nature.
[42] Aditya Joshi,et al. Cleaning up toxic waste: Removing nefarious contributions to recommendation systems , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[43] U. Ernst,et al. Perceptual Inference Predicts Contextual Modulations of Sensory Responses , 2012, The Journal of Neuroscience.
[44] Gábor Szabó,et al. Cannabinoid sensitivity and synaptic properties of 2 GABAergic networks in the neocortex. , 2008, Cerebral cortex.
[45] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[46] E. B. Baum,et al. Internal representations for associative memory , 1988, Biological Cybernetics.
[47] H. Adesnik,et al. A neural circuit for spatial summation in visual cortex , 2012, Nature.
[48] Christopher J. Rozell,et al. Optimal Sparse Approximation with Integrate and Fire Neurons , 2014, Int. J. Neural Syst..
[49] Kechen Zhang,et al. How to Modify a Neural Network Gradually Without Changing Its Input-Output Functionality , 2010, Neural Computation.
[50] Maria V. Sanchez-Vives,et al. Lack of orientation and direction selectivity in a subgroup of fast-spiking inhibitory interneurons: cellular and synaptic mechanisms and comparison with other electrophysiological cell types. , 2008, Cerebral cortex.
[51] 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..
[52] W. Maass,et al. State-dependent computations: spatiotemporal processing in cortical networks , 2009, Nature Reviews Neuroscience.
[53] Michael J. Watts,et al. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[54] M. Brecht,et al. Sparse and powerful cortical spikes , 2010, Current Opinion in Neurobiology.
[55] R. Yuste,et al. Dense, Unspecific Connectivity of Neocortical Parvalbumin-Positive Interneurons: A Canonical Microcircuit for Inhibition? , 2011, The Journal of Neuroscience.
[56] Nikos K Logothetis,et al. Statistical comparison of spike responses to natural stimuli in monkey area V1 with simulated responses of a detailed laminar network model for a patch of V1. , 2011, Journal of neurophysiology.
[57] Karl Deisseroth,et al. Activation of Specific Interneurons Improves V1 Feature Selectivity and Visual Perception , 2012, Nature.
[58] E. Callaway,et al. Excitatory cortical neurons form fine-scale functional networks , 2005, Nature.
[59] Terrence J Sejnowski,et al. Communication in Neuronal Networks , 2003, Science.
[60] G. Sapiro,et al. A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography. , 2013, Journal of structural biology.
[61] M. A. Repucci,et al. Spatial Structure and Symmetry of Simple-Cell Receptive Fields in Macaque Primary Visual Cortex , 2002 .
[62] Hongkui Zeng,et al. Differential tuning and population dynamics of excitatory and inhibitory neurons reflect differences in local intracortical connectivity , 2011, Nature Neuroscience.
[63] David J. Field,et al. How Close Are We to Understanding V1? , 2005, Neural Computation.
[64] Yi Ma,et al. Robust principal component analysis? , 2009, JACM.
[65] C. Niell,et al. What can mice tell us about how vision works? , 2011, Trends in Neurosciences.
[66] H. S. Meyer,et al. Cellular organization of cortical barrel columns is whisker-specific , 2013, Proceedings of the National Academy of Sciences.
[67] A. Koulakov,et al. Sparse Incomplete Representations: A Potential Role of Olfactory Granule Cells , 2011, Neuron.
[68] Aapo Hyvärinen,et al. Natural Image Statistics - A Probabilistic Approach to Early Computational Vision , 2009, Computational Imaging and Vision.
[69] P. Strata,et al. Dale’s principle , 1999, Brain Research Bulletin.
[70] Jose-Manuel Alonso,et al. Functionally distinct inhibitory neurons at the first stage of visual cortical processing , 2003, Nature Neuroscience.
[71] R. Reid,et al. Specificity and randomness: structure–function relationships in neural circuits , 2011, Current Opinion in Neurobiology.
[72] D. Ferster,et al. Neural mechanisms of orientation selectivity in the visual cortex. , 2000, Annual review of neuroscience.
[73] H. Markram,et al. Interneurons of the neocortical inhibitory system , 2004, Nature Reviews Neuroscience.
[74] Eero P. Simoncelli,et al. On Advances in Statistical Modeling of Natural Images , 2004, Journal of Mathematical Imaging and Vision.