Estimating the dimensionality of the manifold underlying multi-electrode neural recordings
暂无分享,去创建一个
[1] D. Ruelle,et al. Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems , 1992 .
[2] Giovanni Bussi,et al. Predicting the Kinetics of RNA Oligonucleotides Using Markov State Models. , 2016, Journal of chemical theory and computation.
[3] P. Grassberger,et al. Measuring the Strangeness of Strange Attractors , 1983 .
[4] W. Newsome,et al. Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.
[5] Byron M. Yu,et al. Neural constraints on learning , 2014, Nature.
[6] G. La Camera,et al. Stimuli Reduce the Dimensionality of Cortical Activity , 2015, bioRxiv.
[7] Konrad P Kording,et al. How advances in neural recording affect data analysis , 2011, Nature Neuroscience.
[8] A. Faisal,et al. Noise in the nervous system , 2008, Nature Reviews Neuroscience.
[9] Matemáticas. Nonlinear Dimensionality Reduction , 2013 .
[10] Anqi Wu,et al. Gaussian process based nonlinear latent structure discovery in multivariate spike train data , 2017, NIPS.
[11] Nicholas A. Steinmetz,et al. High-dimensional geometry of population responses in visual cortex , 2018, Nature.
[12] J. Horn. A rationale and test for the number of factors in factor analysis , 1965, Psychometrika.
[13] Antonino Staiano,et al. Intrinsic dimension estimation: Advances and open problems , 2016, Inf. Sci..
[14] Mei Ying Boon,et al. The correlation dimension: a useful objective measure of the transient visual evoked potential? , 2008, Journal of vision.
[15] Surya Ganguli,et al. A theory of multineuronal dimensionality, dynamics and measurement , 2017, bioRxiv.
[16] Jochen Einbeck,et al. Intrinsic Dimensionality Estimation for High-dimensional Data Sets: New Approaches for the Computation of Correlation Dimension , 2013 .
[17] Krishna V. Shenoy,et al. Accurate Estimation of Neural Population Dynamics without Spike Sorting , 2019, Neuron.
[18] P. McCullagh,et al. Generalized Linear Models , 1972, Predictive Analytics.
[19] Eva L. Dyer,et al. Latent Factors and Dynamics in Motor Cortex and Their Application to Brain–Machine Interfaces , 2018, The Journal of Neuroscience.
[20] John P. Cunningham,et al. Methods for estimating neural firing rates, and their application to brain-machine interfaces , 2009, Neural Networks.
[21] R. Fisher. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .
[22] Chethan Pandarinath,et al. Inferring single-trial neural population dynamics using sequential auto-encoders , 2017, Nature Methods.
[23] Andrei Yu. Zinovyev,et al. Estimating the effective dimension of large biological datasets using Fisher separability analysis , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).
[24] Brent Doiron,et al. Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models , 2016, PLoS Comput. Biol..
[25] Matthew T. Kaufman,et al. Neural population dynamics during reaching , 2012, Nature.
[26] Ivan Tyukin,et al. High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality , 2020, Entropy.
[27] Christian K. Machens,et al. Behavioral / Systems / Cognitive Functional , But Not Anatomical , Separation of “ What ” and “ When ” in Prefrontal Cortex , 2009 .
[28] Lee E. Miller,et al. Long-term stability of cortical population dynamics underlying consistent behavior , 2019, Nature Neuroscience.
[29] G. Laurent,et al. Transient Dynamics versus Fixed Points in Odor Representations by Locust Antennal Lobe Projection Neurons , 2005, Neuron.
[30] Surya Ganguli,et al. Accurate Estimation of Neural Population Dynamics without Spike Sorting , 2017, Neuron.
[31] E. Evarts. Pyramidal tract activity associated with a conditioned hand movement in the monkey. , 1966, Journal of neurophysiology.
[32] Nicholas A. Steinmetz,et al. Spontaneous behaviors drive multidimensional, brainwide activity , 2019, Science.
[33] David Sussillo,et al. Making brain–machine interfaces robust to future neural variability , 2016, Nature communications.
[34] Yoshua Bengio,et al. Adversarial Domain Adaptation for Stable Brain-Machine Interfaces , 2018, ICLR.
[35] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[36] Brent Doiron,et al. Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction , 2018, Current Opinion in Neurobiology.
[37] A. Vinciarelli,et al. Estimating the Intrinsic Dimension of Data with a Fractal-Based Method , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[38] P. Grassberger,et al. Measuring the Strangeness of Strange Attractors , 1983 .
[39] Francis R. Willett,et al. High performance communication by people with paralysis using an intracortical brain-computer interface , 2017, eLife.
[40] L. Abbott,et al. Two layers of neural variability , 2012, Nature Neuroscience.
[41] Ivan Tyukin,et al. Correction of AI systems by linear discriminants: Probabilistic foundations , 2018, Inf. Sci..
[42] Adrienne L. Fairhall,et al. Dimensionality reduction in neuroscience , 2016, Current Biology.
[43] Surya Ganguli,et al. On simplicity and complexity in the brave new world of large-scale neuroscience , 2015, Current Opinion in Neurobiology.
[44] John P. Cunningham,et al. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity , 2008, NIPS.
[45] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[46] Leonard A. Smith. Intrinsic limits on dimension calculations , 1988 .
[47] Jochen Einbeck,et al. On the computation of the correlation integral for fractal dimension estimation , 2012, 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE).
[48] Alessandro Laio,et al. Estimating the intrinsic dimension of datasets by a minimal neighborhood information , 2017, Scientific Reports.
[49] Peter J. Bickel,et al. Maximum Likelihood Estimation of Intrinsic Dimension , 2004, NIPS.
[50] Balázs Kégl,et al. Intrinsic Dimension Estimation Using Packing Numbers , 2002, NIPS.
[51] Emil Wärnberg,et al. Perturbing low dimensional activity manifolds in spiking neuronal networks , 2019, PLoS Comput. Biol..
[52] P. Campadelli,et al. Intrinsic Dimension Estimation: Relevant Techniques and a Benchmark Framework , 2015 .
[53] Lee E. Miller,et al. A neural population mechanism for rapid learning , 2017 .
[54] Byron M. Yu,et al. Stabilization of a brain–computer interface via the alignment of low-dimensional spaces of neural activity , 2020, Nature Biomedical Engineering.
[55] Liam Paninski,et al. Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses , 2016, ICLR.
[56] Shreya Saxena,et al. Towards the neural population doctrine , 2019, Current Opinion in Neurobiology.
[57] Jean-Philippe Vert,et al. NetNorM: Capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis , 2017, PLoS Comput. Biol..
[58] T Kobayashi,et al. Correlation dimension of the human sleep electroencephalogram , 2000, Psychiatry and clinical neurosciences.
[59] Christopher D. Harvey,et al. Choice-specific sequences in parietal cortex during a virtual-navigation decision task , 2012, Nature.
[60] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[61] Matthew T. Kaufman,et al. Supplementary materials for : Cortical activity in the null space : permitting preparation without movement , 2014 .
[62] Francesco Camastra,et al. Data dimensionality estimation methods: a survey , 2003, Pattern Recognit..
[63] Sliman J. Bensmaia,et al. Unexpected complexity of everyday manual behaviors , 2020, Nature Communications.
[64] Jstor. Journal of the Royal Statistical Society. Series A (General) , 1987 .
[65] Lee E. Miller,et al. A Neural Population Mechanism for Rapid Learning , 2017, Neuron.
[66] Byron M. Yu,et al. Dimensionality reduction for large-scale neural recordings , 2014, Nature Neuroscience.
[67] Xiao-Jing Wang,et al. The importance of mixed selectivity in complex cognitive tasks , 2013, Nature.
[68] A. Buja,et al. Remarks on Parallel Analysis. , 1992, Multivariate behavioral research.
[69] Lee E. Miller,et al. Neural Manifolds for the Control of Movement , 2017, Neuron.
[70] Abigail A. Russo,et al. Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response , 2018, Neuron.
[71] Surya Ganguli,et al. Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis , 2017, Neuron.
[72] Gamaleldin F. Elsayed,et al. Structure in neural population recordings: an expected byproduct of simpler phenomena? , 2017, Nature Neuroscience.
[73] John P. Cunningham,et al. Linear dynamical neural population models through nonlinear embeddings , 2016, NIPS.
[74] Christian Ethier,et al. Cortical population activity within a preserved neural manifold underlies multiple motor behaviors , 2018, Nature Communications.