Nonparametric Modeling of Neural Point Processes via Stochastic Gradient Boosting Regression
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[1] J. Copas. Regression, Prediction and Shrinkage , 1983 .
[2] S. Wood. Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models , 2004 .
[3] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[4] Robert E. Kass,et al. A Spike-Train Probability Model , 2001, Neural Computation.
[5] V. Solo,et al. Contrasting Patterns of Receptive Field Plasticity in the Hippocampus and the Entorhinal Cortex: An Adaptive Filtering Approach , 2002, The Journal of Neuroscience.
[6] T. Kneib,et al. BayesX: Analyzing Bayesian Structural Additive Regression Models , 2005 .
[7] Timothy J. Robinson,et al. Sequential Monte Carlo Methods in Practice , 2003 .
[8] J. Friedman. Multivariate adaptive regression splines , 1990 .
[9] L. Paninski,et al. Spatiotemporal tuning of motor cortical neurons for hand position and velocity. , 2004, Journal of neurophysiology.
[10] Ian H. Stevenson,et al. Bayesian Inference of Functional Connectivity and Network Structure From Spikes , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[11] Dani Gamerman,et al. Sampling from the posterior distribution in generalized linear mixed models , 1997, Stat. Comput..
[12] P. R. Fisk,et al. Distributions in Statistics: Continuous Multivariate Distributions , 1971 .
[13] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[14] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[15] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[16] Yoav Freund,et al. Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.
[17] R E Kass,et al. Recursive bayesian decoding of motor cortical signals by particle filtering. , 2004, Journal of neurophysiology.
[18] Jerome H. Friedman. Multivariate adaptive regression splines (with discussion) , 1991 .
[19] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[20] Emery N. Brown,et al. Computational Neuroscience: A Comprehensive Approach , 2022 .
[21] Paul H. C. Eilers,et al. Flexible smoothing with B-splines and penalties , 1996 .
[22] N. G. Best,et al. WinBUGS User Manual: Version 1.4 , 2001 .
[23] Milton C. Chew. Distributions in Statistics: Continuous Univariate Distributions-1 and 2 , 1971 .
[24] W. R. Buckland,et al. Distributions in Statistics: Continuous Multivariate Distributions , 1973 .
[25] David J. Spiegelhalter,et al. WinBUGS user manual version 1.4 , 2003 .
[26] Leo Breiman,et al. Prediction Games and Arcing Algorithms , 1999, Neural Computation.
[27] John D. Lafferty,et al. Boosting and Maximum Likelihood for Exponential Models , 2001, NIPS.
[28] Eberhard E Fetz,et al. Characteristic membrane potential trajectories in primate sensorimotor cortex neurons recorded in vivo. , 2005, Journal of neurophysiology.
[29] Uri T Eden,et al. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. , 2005, Journal of neurophysiology.
[30] Tong Zhang. Statistical behavior and consistency of classification methods based on convex risk minimization , 2003 .
[31] Andreas Brezger,et al. Generalized structured additive regression based on Bayesian P-splines , 2006, Comput. Stat. Data Anal..
[32] E. S. Chornoboy,et al. Maximum likelihood identification of neural point process systems , 1988, Biological Cybernetics.
[33] G. Lugosi,et al. On the Bayes-risk consistency of regularized boosting methods , 2003 .
[34] Matthew A. Wilson,et al. Construction and analysis of non-Gaussian spatial models of neural spiking activity , 2002, Neurocomputing.
[35] Nicholas Hatsopoulos,et al. Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles. , 2004, Journal of neurophysiology.
[36] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[37] Michael J. Black,et al. Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex , 2001, NIPS.
[38] L. Paninski. Maximum likelihood estimation of cascade point-process neural encoding models , 2004, Network.
[39] F. Papangelou. Integrability of expected increments of point processes and a related random change of scale , 1972 .
[40] D. Brillinger. Maximum likelihood analysis of spike trains of interacting nerve cells , 2004, Biological Cybernetics.
[41] Emery N. Brown,et al. Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering , 2004, Neural Computation.
[42] L. Breiman. Arcing the edge , 1997 .
[43] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[44] M. Wilson,et al. Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity , 2005, Neural Computation.
[45] Emery N. Brown,et al. The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis , 2002, Neural Computation.
[46] Ji Zhu,et al. Boosting as a Regularized Path to a Maximum Margin Classifier , 2004, J. Mach. Learn. Res..
[47] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[48] R. Kass,et al. Spline‐based non‐parametric regression for periodic functions and its application to directional tuning of neurons , 2005, Statistics in medicine.
[49] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[50] Trevor Hastie,et al. Additive Logistic Regression : a Statistical , 1998 .
[51] Daryl J. Daley,et al. An Introduction to the Theory of Point Processes , 2013 .
[52] I. V. Girsanov. On Transforming a Certain Class of Stochastic Processes by Absolutely Continuous Substitution of Measures , 1960 .
[53] Peter L. Bartlett,et al. Boosting Algorithms as Gradient Descent , 1999, NIPS.
[54] J. Friedman. Stochastic gradient boosting , 2002 .
[55] R. Kass,et al. Bayesian curve-fitting with free-knot splines , 2001 .