Frontiers in Computational Neuroscience Correlation-based Analysis and Generation of Multiple Spike Trains Using Hawkes Models with an Exogenous Input
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Shy Shoham | Michael Krumin | Inna Reutsky | J. Macke | T. Toyoizumi | S. Shoham | I. Reutsky | Michael Krumin
[1] Lee E Miller,et al. Inferring functional connections between neurons , 2008, Current Opinion in Neurobiology.
[2] A. Rainoldi,et al. Part II , 2012 .
[3] Eero P. Simoncelli,et al. Spatio-temporal correlations and visual signalling in a complete neuronal population , 2008, Nature.
[4] Don H. Johnson,et al. Point process models of single-neuron discharges , 1996, Journal of Computational Neuroscience.
[5] E J Chichilnisky,et al. A simple white noise analysis of neuronal light responses , 2001, Network.
[6] D. Q. Nykamp,et al. A mathematical framework for inferring connectivity in probabilistic neuronal networks. , 2007, Mathematical biosciences.
[7] P. Brémaud,et al. Power spectra of general shot noises and Hawkes point processes with a random excitation , 2002, Advances in Applied Probability.
[8] Kamiar Rahnama Rad,et al. Mean-Field Approximations for Coupled Populations of Generalized Linear Model Spiking Neurons with Markov Refractoriness , 2009, Neural Computation.
[9] Romain Brette,et al. Generation of Correlated Spike Trains , 2009, Neural Computation.
[10] Shy Shoham,et al. Generation of Spike Trains with Controlled Auto- and Cross-Correlation Functions , 2009, Neural Computation.
[11] R. Kass,et al. Multiple neural spike train data analysis: state-of-the-art and future challenges , 2004, Nature Neuroscience.
[12] L. Paninski,et al. Superlinear Population Encoding of Dynamic Hand Trajectory in Primary Motor Cortex , 2004, The Journal of Neuroscience.
[13] Alexander S. Ecker,et al. Generating Spike Trains with Specified Correlation Coefficients , 2009, Neural Computation.
[14] Dario L. Ringach,et al. Reverse correlation in neurophysiology , 2004, Cogn. Sci..
[15] H. L. Bryant,et al. Identification of synaptic interactions , 1976, Biological Cybernetics.
[16] Lennart Ljung,et al. System Identification: Theory for the User , 1987 .
[17] A. Hawkes. Spectra of some self-exciting and mutually exciting point processes , 1971 .
[18] Matthew Fellows,et al. Statistical encoding model for a primary motor cortical brain-machine interface , 2005, IEEE Transactions on Biomedical Engineering.
[19] Liam Paninski,et al. Statistical models for neural encoding, decoding, and optimal stimulus design. , 2007, Progress in brain research.
[20] Diego A Gutnisky,et al. Generation of spatiotemporally correlated spike trains and local field potentials using a multivariate autoregressive process. , 2010, Journal of neurophysiology.
[21] D. Brillinger. Maximum likelihood analysis of spike trains of interacting nerve cells , 2004, Biological Cybernetics.
[22] C. Granger. Investigating causal relations by econometric models and cross-spectral methods , 1969 .
[23] A. Hawkes. Point Spectra of Some Mutually Exciting Point Processes , 1971 .
[24] Fred Wolf,et al. Correlations and synchrony in threshold neuron models. , 2008, Physical review letters.
[25] E. S. Chornoboy,et al. Maximum likelihood identification of neural point process systems , 1988, Biological Cybernetics.
[26] B. Noble,et al. Methods Based on the Wiener-Hopf Technique. , 1960 .
[27] Shy Shoham,et al. Correlation-distortion based identification of Linear-Nonlinear-Poisson models , 2009, Journal of Computational Neuroscience.
[28] Shy Shoham,et al. Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains , 2010, Comput. Intell. Neurosci..
[29] D. Brillinger. The Identification of Point Process Systems , 1975 .