Hidden Markov Models for the Stimulus-Response Relationships of Multistate Neural Systems
暂无分享,去创建一个
Liam Paninski | Sean Escola | Alfredo Fontanini | Don Katz | L. Paninski | G. S. Escola | A. Fontanini | Don Katz | Sean Escola
[1] L. Baum,et al. A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .
[2] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[3] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[4] G. Wahba. Spline models for observational data , 1990 .
[5] K. Chan,et al. Monte Carlo EM Estimation for Time Series Models Involving Counts , 1995 .
[6] Naftali Tishby,et al. Cortical activity flips among quasi-stationary states. , 1995, Proceedings of the National Academy of Sciences of the United States of America.
[7] E. Seidemann,et al. Simultaneously recorded single units in the frontal cortex go through sequences of discrete and stable states in monkeys performing a delayed localization task , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[8] Naftali Tishby,et al. Hidden Markov modelling of simultaneously recorded cells in the associative cortex of behaving monkeys , 1997 .
[9] S. Sherman,et al. Burst and tonic firing in thalamic cells of unanesthetized, behaving monkeys , 2000, Visual Neuroscience.
[10] Maria V. Sanchez-Vives,et al. Cellular and network mechanisms of rhythmic recurrent activity in neocortex , 2000, Nature Neuroscience.
[11] M. Carandini,et al. Stimulus dependence of two-state fluctuations of membrane potential in cat visual cortex , 2000, Nature Neuroscience.
[12] E N Brown,et al. An analysis of neural receptive field plasticity by point process adaptive filtering , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[13] Robert E. Kass,et al. A Spike-Train Probability Model , 2001, Neural Computation.
[14] J. Sansom,et al. Fitting hidden semi-Markov models to breakpoint rainfall data , 2001, Journal of Applied Probability.
[15] S. Sherman. Tonic and burst firing: dual modes of thalamocortical relay , 2001, Trends in Neurosciences.
[16] 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.
[17] M. Merzenich,et al. Changes of AI receptive fields with sound density. , 2002, Journal of neurophysiology.
[18] Emery N. Brown,et al. Estimating a State-space Model from Point Process Observations Emery N. Brown , 2022 .
[19] R. Kass,et al. Statistical smoothing of neuronal data. , 2003, Network.
[20] Zoubin Ghahramani,et al. Optimization with EM and Expectation-Conjugate-Gradient , 2003, ICML.
[21] Y. Guédon. Estimating Hidden Semi-Markov Chains From Discrete Sequences , 2003 .
[22] L. Paninski. Maximum likelihood estimation of cascade point-process neural encoding models , 2004, Network.
[23] Michael J. Black,et al. Modeling and decoding motor cortical activity using a switching Kalman filter , 2004, IEEE Transactions on Biomedical Engineering.
[24] Emery N. Brown,et al. Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering , 2004, Neural Computation.
[25] 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 .
[26] Byron M. Yu,et al. Extracting Dynamical Structure Embedded in Neural Activity , 2005, NIPS.
[27] H. Sompolinsky,et al. Adaptation without parameter change: Dynamic gain control in motion detection , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[28] 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.
[29] D. Katz,et al. State-dependent modulation of time-varying gustatory responses. , 2006, Journal of neurophysiology.
[30] J. Alonso,et al. Thalamic Burst Mode and Inattention in the Awake LGNd , 2006, Neuron.
[31] L. Paninski,et al. Common-input models for multiple neural spike-train data , 2007, Network.
[32] L. Paninski,et al. Neural decoding of goal-directed movements using a linear state-space model with hidden states , 2007 .
[33] D. McCormick,et al. Enhancement of visual responsiveness by spontaneous local network activity in vivo. , 2007, Journal of neurophysiology.
[34] Paul Miller,et al. Natural stimuli evoke dynamic sequences of states in sensory cortical ensembles , 2007, Proceedings of the National Academy of Sciences.
[35] Qingbo Wang,et al. Feedforward Excitation and Inhibition Evoke Dual Modes of Firing in the Cat's Visual Thalamus during Naturalistic Viewing , 2007, Neuron.
[36] Liam Paninski,et al. Statistical models for neural encoding, decoding, and optimal stimulus design. , 2007, Progress in brain research.
[37] A. Fairhall,et al. Shifts in Coding Properties and Maintenance of Information Transmission during Adaptation in Barrel Cortex , 2007, PLoS biology.
[38] Michael I. Jordan,et al. An HDP-HMM for systems with state persistence , 2008, ICML '08.
[39] Uri T Eden,et al. Analysis of between-trial and within-trial neural spiking dynamics. , 2008, Journal of neurophysiology.
[40] M. Sahani,et al. Nonlinearities and Contextual Influences in Auditory Cortical Responses Modeled with Multilinear Spectrotemporal Methods , 2008, The Journal of Neuroscience.
[41] Byron M. Yu,et al. Detecting neural-state transitions using hidden Markov models for motor cortical prostheses. , 2008, Journal of neurophysiology.
[42] L. Paninski,et al. Inferring input nonlinearities in neural encoding models , 2008, Network.
[43] Adrienne L. Fairhall,et al. Intrinsic Gain Modulation and Adaptive Neural Coding , 2008, PLoS Comput. Biol..
[44] Wei Wu,et al. Neural Decoding of Hand Motion Using a Linear State-Space Model With Hidden States , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[45] Gary Sean Escola. Markov chains, neural responses, and optimal temporal computations , 2009 .
[46] Robert E. Kass,et al. Detection of bursts in extracellular spike trains using hidden semi-Markov point process models , 2010, Journal of Computational Neuroscience.
[47] Matthew A. Wilson,et al. Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States , 2009, Neural Computation.
[48] Wei Wu,et al. A new look at state-space models for neural data , 2010, Journal of Computational Neuroscience.
[49] D. Jin. Generating variable birdsong syllable sequences with branching chain networks in avian premotor nucleus HVC. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.
[50] Kamiar Rahnama Rad,et al. Efficient, adaptive estimation of two-dimensional firing rate surfaces via Gaussian process methods , 2010, Network.