Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis
暂无分享,去创建一个
Tamara G. Kolda | Surya Ganguli | Stephen I. Ryu | Krishna V. Shenoy | Mark J. Schnitzer | Alex H. Williams | Tony Hyun Kim | Saurabh Vyas | Forea Wang | T. Kolda | K. Shenoy | M. Schnitzer | S. Ganguli | S. Ryu | Forea Wang | T. H. Kim | Saurabh Vyas
[1] C. Eckart,et al. The approximation of one matrix by another of lower rank , 1936 .
[2] J. Chang,et al. Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .
[3] Richard A. Harshman,et al. Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .
[4] J. Kruskal. Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics , 1977 .
[5] A P Georgopoulos,et al. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[6] Paul J. Werbos,et al. Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.
[7] J. Movshon,et al. The analysis of visual motion: a comparison of neuronal and psychophysical performance , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[8] Yuichi Nakamura,et al. Approximation of dynamical systems by continuous time recurrent neural networks , 1993, Neural Networks.
[9] P. Paatero,et al. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values† , 1994 .
[10] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[11] H S Seung,et al. How the brain keeps the eyes still. , 1996, Proceedings of the National Academy of Sciences of the United States of America.
[12] R. Bro,et al. A fast non‐negativity‐constrained least squares algorithm , 1997 .
[13] P. Paatero. A weighted non-negative least squares algorithm for three-way ‘PARAFAC’ factor analysis , 1997 .
[14] J. Kleim,et al. Functional reorganization of the rat motor cortex following motor skill learning. , 1998, Journal of neurophysiology.
[15] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[16] Emilio Salinas,et al. Gain Modulation A Major Computational Principle of the Central Nervous System , 2000, Neuron.
[17] Max Welling,et al. Positive tensor factorization , 2001, Pattern Recognit. Lett..
[18] Eric Jones,et al. SciPy: Open Source Scientific Tools for Python , 2001 .
[19] Frances S. Chance,et al. Gain Modulation from Background Synaptic Input , 2002, Neuron.
[20] Emery N. Brown,et al. Estimating a State-space Model from Point Process Observations Emery N. Brown , 2022 .
[21] Tamara G. Kolda,et al. A Counterexample to the Possibility of an Extension of the Eckart-Young Low-Rank Approximation Theorem for the Orthogonal Rank Tensor Decomposition , 2002, SIAM J. Matrix Anal. Appl..
[22] S. Prescott,et al. Gain control of firing rate by shunting inhibition: Roles of synaptic noise and dendritic saturation , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[23] William S Rayens,et al. Structure-seeking multilinear methods for the analysis of fMRI data , 2004, NeuroImage.
[24] I. Dean,et al. Neural population coding of sound level adapts to stimulus statistics , 2005, Nature Neuroscience.
[25] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[26] Rasmus Bro,et al. A comparison of algorithms for fitting the PARAFAC model , 2006, Comput. Stat. Data Anal..
[27] A. Pouget,et al. Neural correlations, population coding and computation , 2006, Nature Reviews Neuroscience.
[28] Lars Kai Hansen,et al. Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG , 2006, NeuroImage.
[29] John D. Hunter,et al. Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.
[30] G. Golub,et al. A tensor higher-order singular value decomposition for integrative analysis of DNA microarray data from different studies , 2007, Proceedings of the National Academy of Sciences.
[31] Rasmus Bro,et al. Multiway analysis of epilepsy tensors , 2007, ISMB/ECCB.
[32] John P. Cunningham,et al. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity , 2008, NIPS.
[33] M. Sahani,et al. Nonlinearities and Contextual Influences in Auditory Cortical Responses Modeled with Multilinear Spectrotemporal Methods , 2008, The Journal of Neuroscience.
[34] Jessica A. Cardin,et al. Cellular Mechanisms Underlying Stimulus-Dependent Gain Modulation in Primary Visual Cortex Neurons In Vivo , 2008, Neuron.
[35] C. Law,et al. Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area , 2008, Nature Neuroscience.
[36] P. Comon,et al. Tensor decompositions, alternating least squares and other tales , 2009 .
[37] Stephen A. Vavasis,et al. On the Complexity of Nonnegative Matrix Factorization , 2007, SIAM J. Optim..
[38] Pierre Comon,et al. Nonnegative approximations of nonnegative tensors , 2009, ArXiv.
[39] Tamara G. Kolda,et al. Tensor Decompositions and Applications , 2009, SIAM Rev..
[40] Bernd Sturmfels,et al. Reconstructing spatiotemporal gene expression data from partial observations , 2009, Bioinform..
[41] Erin L. Rich,et al. Rat Prefrontal Cortical Neurons Selectively Code Strategy Switches , 2009, The Journal of Neuroscience.
[42] J. Carmena,et al. Emergence of a Stable Cortical Map for Neuroprosthetic Control , 2009, PLoS biology.
[43] J. White,et al. Gain Control in CA1 Pyramidal Cells Using Changes in Somatic Conductance , 2010, The Journal of Neuroscience.
[44] S. Kennerley,et al. Heterogeneous reward signals in prefrontal cortex , 2010, Current Opinion in Neurobiology.
[45] M. Stryker,et al. Modulation of Visual Responses by Behavioral State in Mouse Visual Cortex , 2010, Neuron.
[46] D. Durstewitz,et al. Abrupt Transitions between Prefrontal Neural Ensemble States Accompany Behavioral Transitions during Rule Learning , 2010, Neuron.
[47] J. Maunsell,et al. A Neuronal Population Measure of Attention Predicts Behavioral Performance on Individual Trials , 2010, The Journal of Neuroscience.
[48] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[49] A. Gamal,et al. Miniaturized integration of a fluorescence microscope , 2011, Nature Methods.
[50] Johannes J. Letzkus,et al. A disinhibitory microcircuit for associative fear learning in the auditory cortex , 2011, Nature.
[51] Vittorio Ferrari,et al. Advances in Neural Information Processing Systems 24 , 2011 .
[52] Haesun Park,et al. Fast Nonnegative Matrix Factorization: An Active-Set-Like Method and Comparisons , 2011, SIAM J. Sci. Comput..
[53] J. Maunsell,et al. When Attention Wanders: How Uncontrolled Fluctuations in Attention Affect Performance , 2011, The Journal of Neuroscience.
[54] M. Carandini,et al. Normalization as a canonical neural computation , 2011, Nature Reviews Neuroscience.
[55] John P. Cunningham,et al. A High-Performance Neural Prosthesis Enabled by Control Algorithm Design , 2012, Nature Neuroscience.
[56] M. Carandini,et al. Parvalbumin-Expressing Interneurons Linearly Transform Cortical Responses to Visual Stimuli , 2012, Neuron.
[57] Maneesh Sahani,et al. Spectral learning of linear dynamics from generalised-linear observations with application to neural population data , 2012, NIPS.
[58] Tamara G. Kolda,et al. On Tensors, Sparsity, and Nonnegative Factorizations , 2011, SIAM J. Matrix Anal. Appl..
[59] T. Komiyama,et al. Parvalbumin-Expressing Interneurons Linearly Control Olfactory Bulb Output , 2013, Neuron.
[60] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[61] M. Sahani,et al. Cortical control of arm movements: a dynamical systems perspective. , 2013, Annual review of neuroscience.
[62] Jan M. Rabaey,et al. Physical principles for scalable neural recording , 2013, Front. Comput. Neurosci..
[63] N. Sigala,et al. Dynamic Coding for Cognitive Control in Prefrontal Cortex , 2013, Neuron.
[64] W. Newsome,et al. Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.
[65] Christopher J. Hillar,et al. Most Tensor Problems Are NP-Hard , 2009, JACM.
[66] Xiao-Jing Wang,et al. The importance of mixed selectivity in complex cognitive tasks , 2013, Nature.
[67] Dean V. Buonomano,et al. ROBUST TIMING AND MOTOR PATTERNS BY TAMING CHAOS IN RECURRENT NEURAL NETWORKS , 2012, Nature Neuroscience.
[68] F. Helmchen,et al. Steady or changing? Long-term monitoring of neuronal population activity , 2013, Trends in Neurosciences.
[69] Eero P. Simoncelli,et al. Partitioning neuronal variability , 2014, Nature Neuroscience.
[70] David Sussillo,et al. Neural circuits as computational dynamical systems , 2014, Current Opinion in Neurobiology.
[71] Aaron C. Koralek,et al. Volitional modulation of optically recorded calcium signals during neuroprosthetic learning , 2014, Nature Neuroscience.
[72] Simon X. Chen,et al. Emergence of reproducible spatiotemporal activity during motor learning , 2014, Nature.
[73] Byron M. Yu,et al. Dimensionality reduction for large-scale neural recordings , 2014, Nature Neuroscience.
[74] John P. Cunningham,et al. Single-trial dynamics of motor cortex and their applications to brain-machine interfaces , 2015, Nature Communications.
[75] Eero P. Simoncelli,et al. Attention stabilizes the shared gain of V4 , 2015 .
[76] Xiaofeng Gong,et al. Tensor decomposition of EEG signals: A brief review , 2015, Journal of Neuroscience Methods.
[77] Eero P. Simoncelli,et al. Attention stabilizes the shared gain of V4 populations , 2015, eLife.
[78] Rasmus Bro,et al. Data Fusion in Metabolomics Using Coupled Matrix and Tensor Factorizations , 2015, Proceedings of the IEEE.
[79] Surya Ganguli,et al. On simplicity and complexity in the brave new world of large-scale neuroscience , 2015, Current Opinion in Neurobiology.
[80] Guangyu R. Yang,et al. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework , 2016, PLoS Comput. Biol..
[81] John P. Cunningham,et al. Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1 , 2016, PLoS Comput. Biol..
[82] Yuan Zhao,et al. Interpretable Nonlinear Dynamic Modeling of Neural Trajectories , 2016, NIPS.
[83] Michael Z. Lin,et al. Genetically encoded indicators of neuronal activity , 2016, Nature Neuroscience.
[84] Naoshige Uchida,et al. Demixed principal component analysis of neural population data , 2016, eLife.
[85] Scott W. Linderman,et al. Recurrent switching linear dynamical systems , 2016, 1610.08466.
[86] Rajesh Poddar,et al. Automated long-term recording and analysis of neural activity in behaving animals , 2016 .
[87] T. Komiyama,et al. Circuit Mechanisms of Sensorimotor Learning , 2016, Neuron.
[88] Hongkui Zeng,et al. Long-Term Optical Access to an Estimated One Million Neurons in the Live Mouse Cortex. , 2016, Cell reports.
[89] M. Siniscalchi,et al. Fast and slow transitions in frontal ensemble activity during flexible sensorimotor behavior , 2016, Nature Neuroscience.
[90] M. McCarthy,et al. Tensor decomposition for multi-tissue gene expression experiments , 2016, Nature Genetics.
[91] John P. Cunningham,et al. Linear dynamical neural population models through nonlinear embeddings , 2016, NIPS.
[92] Mario Dipoppa,et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy , 2016, bioRxiv.
[93] Pablo E. Jercog,et al. Neural ensemble dynamics underlying a long-term associative memory , 2017, Nature.
[94] Xiao-Jing Wang,et al. Reward-based training of recurrent neural networks for cognitive and value-based tasks , 2016, bioRxiv.
[95] Patrick Dupont,et al. Tensor decompositions and data fusion in epileptic electroencephalography and functional magnetic resonance imaging data , 2017, WIREs Data Mining Knowl. Discov..
[96] Polina Anikeeva,et al. Neural Recording and Modulation Technologies. , 2017, Nature reviews. Materials.
[97] Euisik Yoon,et al. State-of-the-art MEMS and microsystem tools for brain research , 2017, Microsystems & Nanoengineering.
[98] Selmaan N. Chettih,et al. Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex , 2017, Cell.
[99] Konrad Paul Kording,et al. Could a Neuroscientist Understand a Microprocessor? , 2016, bioRxiv.
[100] Chethan Pandarinath,et al. Inferring single-trial neural population dynamics using sequential auto-encoders , 2017 .
[101] Scott W. Linderman,et al. Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems , 2017, AISTATS.
[102] Chethan Pandarinath,et al. Inferring single-trial neural population dynamics using sequential auto-encoders , 2017, Nature Methods.
[103] Maja Pantic,et al. TensorLy: Tensor Learning in Python , 2016, J. Mach. Learn. Res..