Parameter estimation and identifiability in a neural population model for electro-cortical activity
暂无分享,去创建一个
David T. J. Liley | Damien G. Hicks | Peter J. Cadusch | Agus Hartoyo | D. Liley | P. Cadusch | D. G. Hicks | Agus Hartoyo | D. Hicks
[1] P. Welch. The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .
[2] Mark K Transtrum,et al. Geometry of nonlinear least squares with applications to sloppy models and optimization. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.
[3] Lennaert van Veen,et al. Chaos via Shilnikov's saddle-node bifurcation in a theory of the electroencephalogram. , 2006, Physical review letters.
[4] Michael C. Abbott,et al. Maximizing the information learned from finite data selects a simple model , 2017, Proceedings of the National Academy of Sciences.
[5] P. Robinson,et al. Dynamics of large-scale brain activity in normal arousal states and epileptic seizures. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.
[6] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[7] C. Cobelli,et al. Parameter and structural identifiability concepts and ambiguities: a critical review and analysis. , 1980, The American journal of physiology.
[8] J. Reid,et al. Structural identifiability in linear time invariant systems , 1976, 1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes.
[9] N. Birbaumer,et al. BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.
[10] Ingo Bojak,et al. Co-operative Populations of Neurons: Mean Field Models of Mesoscopic Brain Activity , 2012 .
[11] R. Jindra. Mass action in the nervous system W. J. Freeman, Academic Press, New York (1975), 489 pp., (hard covers). $34.50 , 1976, Neuroscience.
[12] François Mauguière,et al. Human thalamic and cortical activities assessed by dimension of activation and spectral edge frequency during sleep wake cycles. , 2007, Sleep.
[13] Remis Balaniuk,et al. Structural identification , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.
[14] David G. Long,et al. The probability density of spectral estimates based on modified periodogram averages , 1999, IEEE Trans. Signal Process..
[15] Mark K Transtrum,et al. Why are nonlinear fits to data so challenging? , 2009, Physical review letters.
[16] Michael J. Aminoff,et al. Chapter 3 – Electroencephalography: General Principles and Clinical Applications , 2005 .
[17] Eric F. Wood,et al. Review and Unification of Linear Identifiability Concepts , 1982 .
[18] B. Moore. Principal component analysis in linear systems: Controllability, observability, and model reduction , 1981 .
[19] P. Wolynes,et al. The middle way. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[20] D. Liley,et al. Drug-induced modification of the system properties associated with spontaneous human electroencephalographic activity. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.
[21] E. Marder,et al. Global Structure, Robustness, and Modulation of Neuronal Models , 2001, The Journal of Neuroscience.
[22] Eduardo D. Sontag. Dynamic compensation, parameter identifiability, and equivariances , 2017, PLoS Comput. Biol..
[23] Andrew White,et al. The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems , 2016, PLoS Comput. Biol..
[24] P. Rapp,et al. Re-examination of the evidence for low-dimensional, nonlinear structure in the human electroencephalogram. , 1996, Electroencephalography and clinical neurophysiology.
[25] Liam Paninski,et al. Efficient estimation of detailed single-neuron models. , 2006, Journal of neurophysiology.
[26] E. Brown,et al. Thalamocortical Mechanisms for the Anteriorization of Alpha Rhythms during Propofol-Induced Unconsciousness , 2013, The Journal of Neuroscience.
[27] U Finsterer,et al. Spectral edge frequency of the electroencephalogram to monitor "depth" of anaesthesia with isoflurane or propofol. , 1996, British journal of anaesthesia.
[28] J. Nemcová. Structural identifiability of polynomial and rational systems. , 2008, Mathematical biosciences.
[29] Russell C. Eberhart,et al. A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.
[30] K. S. Brown,et al. Sloppy-model universality class and the Vandermonde matrix. , 2006, Physical review letters.
[31] David T. J. Liley,et al. A continuum theory of electro-cortical activity , 1999, Neurocomputing.
[32] Mark S. Goldman,et al. A Modeling Framework for Deriving the Structural and Functional Architecture of a Short-Term Memory Microcircuit , 2013, Neuron.
[33] Ronald L. Calabrese,et al. Identifying Crucial Parameter Correlations Maintaining Bursting Activity , 2014, PLoS Comput. Biol..
[34] Ursula Kummer,et al. A New Time-Dependent Complexity Reduction Method for Biochemical Systems , 2005, Trans. Comp. Sys. Biology.
[35] S. Andersson,et al. Physiological basis of the alpha rhythm , 1968 .
[36] Karl J. Friston,et al. The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields , 2008, PLoS Comput. Biol..
[37] Mark K Transtrum,et al. Model reduction by manifold boundaries. , 2014, Physical review letters.
[38] Donald L Rowe,et al. Estimation of neurophysiological parameters from the waking EEG using a biophysical model of brain dynamics. , 2004, Journal of theoretical biology.
[39] Edda Klipp,et al. Biochemical network models simplified by balanced truncation , 2005, The FEBS journal.
[40] J. Sethna,et al. Parameter Space Compression Underlies Emergent Theories and Predictive Models , 2013, Science.
[41] N. Goldenfeld. Lectures On Phase Transitions And The Renormalization Group , 1972 .
[42] Bryan C. Daniels,et al. Perspective: Sloppiness and emergent theories in physics, biology, and beyond. , 2015, The Journal of chemical physics.
[43] Ursula Klingmüller,et al. Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood , 2009, Bioinform..
[44] J. Gross,et al. Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints , 2016, PLoS biology.
[45] Antonis Papachristodoulou,et al. Structural Identifiability of Dynamic Systems Biology Models , 2016, PLoS Comput. Biol..
[46] J. Banga,et al. Structural Identifiability of Systems Biology Models: A Critical Comparison of Methods , 2011, PloS one.
[47] J. Kropotov. Quantitative EEG, Event-Related Potentials and Neurotherapy , 2008 .
[48] Dominique L. Pritchett,et al. Quantitative analysis and biophysically realistic neural modeling of the MEG mu rhythm: rhythmogenesis and modulation of sensory-evoked responses. , 2009, Journal of neurophysiology.
[49] Nicolas Le Novère,et al. Computational Systems Neurobiology , 2012, Springer Netherlands.
[50] Peng Qiu,et al. Bridging Mechanistic and Phenomenological Models of Complex Biological Systems , 2015, PLoS Comput. Biol..
[51] M. Knyazeva,et al. Fine Structure of Posterior Alpha Rhythm in Human EEG: Frequency Components, Their Cortical Sources, and Temporal Behavior , 2017, Scientific Reports.
[52] J. Willems,et al. Parametrizations of linear dynamical systems: Canonical forms and identifiability , 1974 .
[53] Julio R. Banga,et al. Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation , 2017, PLoS Comput. Biol..
[54] J. Bruhn,et al. Correlation of Approximate Entropy, Bispectral Index, and Spectral Edge Frequency 95 (SEF95) with Clinical Signs of “Anesthetic Depth” during Coadministration of Propofol and Remifentanil , 2003, Anesthesiology.
[55] U. Alon,et al. Dynamical compensation in physiological circuits , 2016, Molecular systems biology.
[56] Mathew P. Dafilis,et al. A spatially continuous mean field theory of electrocortical activity , 2002, Network.
[57] J. Fermaglich. Electric Fields of the Brain: The Neurophysics of EEG , 1982 .
[58] H. Berger. On the electroencephalogram of man. , 1969, Electroencephalography and clinical neurophysiology.
[59] Asohan Amarasingham,et al. Ambiguity and nonidentifiability in the statistical analysis of neural codes , 2015, Proceedings of the National Academy of Sciences.
[60] Christopher R. Myers,et al. Universally Sloppy Parameter Sensitivities in Systems Biology Models , 2007, PLoS Comput. Biol..
[61] S. Hughes,et al. Thalamic Mechanisms of EEG Alpha Rhythms and Their Pathological Implications , 2005, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[62] Lennart Ljung,et al. On global identifiability for arbitrary model parametrizations , 1994, Autom..
[63] F. L. D. Silva,et al. Dynamics of the human alpha rhythm: evidence for non-linearity? , 1999, Clinical Neurophysiology.
[64] Juri D. Kropotov. Chapter 2 – Alpha Rhythms , 2009 .
[65] Diego Lozano-Soldevilla,et al. On the Physiological Modulation and Potential Mechanisms Underlying Parieto-Occipital Alpha Oscillations , 2018, Front. Comput. Neurosci..
[66] Ingo Bojak,et al. Self-organized 40Hz synchronization in a physiological theory of EEG , 2007, Neurocomputing.
[67] K. H. Lee,et al. The statistical mechanics of complex signaling networks: nerve growth factor signaling , 2004, Physical biology.
[68] Mustafa Khammash,et al. Parameter Estimation and Model Selection in Computational Biology , 2010, PLoS Comput. Biol..
[69] Stephen Coombes,et al. Large-scale neural dynamics: Simple and complex , 2010, NeuroImage.
[70] Erik De Schutter. Why Are Computational Neuroscience and Systems Biology So Separate? , 2008, PLoS Comput. Biol..
[71] D. Liley,et al. Robust chaos in a model of the electroencephalogram: Implications for brain dynamics. , 2001, Chaos.
[72] E. Arredondo,et al. Council of Europe Black Sea Area Project: International Cooperation for the Development of Activities Related to Donation and Transplantation of Organs in the Region. , 2018, Transplantation proceedings.
[73] Evgueni A. Haroutunian,et al. Information Theory and Statistics , 2011, International Encyclopedia of Statistical Science.
[74] Michael J. Aminoff,et al. Aminoff's electrodiagnosis in clinical neurology / , 2012 .
[75] E. Marder,et al. How Multiple Conductances Determine Electrophysiological Properties in a Multicompartment Model , 2009, The Journal of Neuroscience.
[76] I. Bojak,et al. Fast approximate Bayesian inference for stable differential equation models , 2017, 1706.00689.
[77] Clemens Kreutz. An easy and efficient approach for testing identifiability , 2018, Bioinform..
[78] Arild Thowsen,et al. Structural identifiability , 1977, 1977 IEEE Conference on Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications.
[79] Julio R. Banga,et al. Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems , 2017, BMC Systems Biology.
[80] Jeffrey M. Hausdorff,et al. Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .
[81] E. Marder,et al. Similar network activity from disparate circuit parameters , 2004, Nature Neuroscience.
[82] H Berger. On the electroencephalogram of man. Third report. , 1969, Electroencephalography and clinical neurophysiology.
[83] Stefan Klöppel,et al. Assessing parameter identifiability for dynamic causal modeling of fMRI data , 2015, Front. Neurosci..
[84] H. Berger. Über das Elektrenkephalogramm des Menschen , 1938, Archiv für Psychiatrie und Nervenkrankheiten.
[85] Xiaohua Xia,et al. On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics , 2011, SIAM Rev..
[86] Antonis Papachristodoulou,et al. Delineating parameter unidentifiabilities in complex models. , 2016, Physical review. E.
[87] K. Glover,et al. Identifiability of linear and nonlinear dynamical systems , 1976 .
[88] A. Shapiro. Monte Carlo Sampling Methods , 2003 .
[89] D. Liley,et al. Modeling the effects of anesthesia on the electroencephalogram. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.
[90] Bryan C. Daniels,et al. Sloppiness, robustness, and evolvability in systems biology. , 2008, Current opinion in biotechnology.
[91] K. S. Brown,et al. Statistical mechanical approaches to models with many poorly known parameters. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.
[92] Irina Surovtsova,et al. Simplification of biochemical models: a general approach based on the analysis of the impact of individual species and reactions on the systems dynamics , 2011, BMC Systems Biology.
[93] Axel Hutt,et al. Optimal Model Parameter Estimation from EEG Power Spectrum Features Observed during General Anesthesia , 2018, Neuroinformatics.
[94] Daniel E. Zak,et al. Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network. , 2003, Genome research.
[95] Eva Balsa-Canto,et al. On the relationship between sloppiness and identifiability. , 2016, Mathematical biosciences.
[96] David Swigon,et al. Identifiability of Linear and Linear-in-Parameters Dynamical Systems from a Single Trajectory , 2014, SIAM J. Appl. Dyn. Syst..
[97] Huaiyu Zhu. On Information and Sufficiency , 1997 .
[98] Yue Shi,et al. A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).
[99] D. Sorensen,et al. Approximation of large-scale dynamical systems: an overview , 2004 .
[100] Jens Timmer,et al. Likelihood based observability analysis and confidence intervals for predictions of dynamic models , 2011, BMC Systems Biology.
[101] Johan Karlsson,et al. Comparison of approaches for parameter identifiability analysis of biological systems , 2014, Bioinform..
[102] Eve Marder,et al. Computational models in the age of large datasets , 2015, Current Opinion in Neurobiology.