918 related articles for article (PubMed ID: 28062244)
1. Identification of time-varying neural dynamics from spike train data using multiwavelet basis functions.
Xu S; Li Y; Guo Q; Yang XF; Chan RHM
J Neurosci Methods; 2017 Feb; 278():46-56. PubMed ID: 28062244
[TBL] [Abstract][Full Text] [Related]
2. Time-Varying System Identification Using an Ultra-Orthogonal Forward Regression and Multiwavelet Basis Functions With Applications to EEG.
Li Y; Cui WG; Guo YZ; Huang T; Yang XF; Wei HL
IEEE Trans Neural Netw Learn Syst; 2018 Jul; 29(7):2960-2972. PubMed ID: 28650829
[TBL] [Abstract][Full Text] [Related]
3. Identification of time-varying neural dynamics from spiking activities using Chebyshev polynomials.
Song Xu ; Yang Li ; Xudong Wang ; Chan RH
Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():1591-1594. PubMed ID: 28268632
[TBL] [Abstract][Full Text] [Related]
4. System identification of point-process neural systems using probability based Volterra kernels.
Sandler RA; Deadwyler SA; Hampson RE; Song D; Berger TW; Marmarelis VZ
J Neurosci Methods; 2015 Jan; 240():179-92. PubMed ID: 25479231
[TBL] [Abstract][Full Text] [Related]
5. Adaptive inverse control of neural spatiotemporal spike patterns with a reproducing kernel Hilbert space (RKHS) framework.
Li L; Park IM; Brockmeier A; Chen B; Seth S; Francis JT; Sanchez JC; Príncipe JC
IEEE Trans Neural Syst Rehabil Eng; 2013 Jul; 21(4):532-43. PubMed ID: 22868633
[TBL] [Abstract][Full Text] [Related]
6. Nonlinear dynamic modeling of spike train transformations for hippocampal-cortical prostheses.
Song D; Chan RH; Marmarelis VZ; Hampson RE; Deadwyler SA; Berger TW
IEEE Trans Biomed Eng; 2007 Jun; 54(6 Pt 1):1053-66. PubMed ID: 17554824
[TBL] [Abstract][Full Text] [Related]
7. State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data.
Shimazaki H; Amari S; Brown EN; Grün S
PLoS Comput Biol; 2012; 8(3):e1002385. PubMed ID: 22412358
[TBL] [Abstract][Full Text] [Related]
8. An analysis of neural receptive field plasticity by point process adaptive filtering.
Brown EN; Nguyen DP; Frank LM; Wilson MA; Solo V
Proc Natl Acad Sci U S A; 2001 Oct; 98(21):12261-6. PubMed ID: 11593043
[TBL] [Abstract][Full Text] [Related]
9. Sequential Monte Carlo point-process estimation of kinematics from neural spiking activity for brain-machine interfaces.
Wang Y; Paiva AR; Príncipe JC; Sanchez JC
Neural Comput; 2009 Oct; 21(10):2894-930. PubMed ID: 19548797
[TBL] [Abstract][Full Text] [Related]
10. Total spiking probability edges: A cross-correlation based method for effective connectivity estimation of cortical spiking neurons.
De Blasi S; Ciba M; Bahmer A; Thielemann C
J Neurosci Methods; 2019 Jan; 312():169-181. PubMed ID: 30500352
[TBL] [Abstract][Full Text] [Related]
11. Laguerre-Volterra identification of spike-timing-dependent plasticity from spiking activity: a simulation study.
Robinson BS; Song D; Berger TW
Annu Int Conf IEEE Eng Med Biol Soc; 2013; 2013():5578-81. PubMed ID: 24111001
[TBL] [Abstract][Full Text] [Related]
12. Identification of functional synaptic plasticity from spiking activities using nonlinear dynamical modeling.
Song D; Chan RH; Robinson BS; Marmarelis VZ; Opris I; Hampson RE; Deadwyler SA; Berger TW
J Neurosci Methods; 2015 Apr; 244():123-35. PubMed ID: 25280984
[TBL] [Abstract][Full Text] [Related]
13. The time-rescaling theorem and its application to neural spike train data analysis.
Brown EN; Barbieri R; Ventura V; Kass RE; Frank LM
Neural Comput; 2002 Feb; 14(2):325-46. PubMed ID: 11802915
[TBL] [Abstract][Full Text] [Related]
14. Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation.
Kim H; Shinomoto S
Math Biosci Eng; 2014 Feb; 11(1):49-62. PubMed ID: 24245682
[TBL] [Abstract][Full Text] [Related]
15. A reproducing kernel Hilbert space framework for spike train signal processing.
Paiva AR; Park I; Príncipe JC
Neural Comput; 2009 Feb; 21(2):424-49. PubMed ID: 19431265
[TBL] [Abstract][Full Text] [Related]
16. Estimating nonstationary input signals from a single neuronal spike train.
Kim H; Shinomoto S
Phys Rev E Stat Nonlin Soft Matter Phys; 2012 Nov; 86(5 Pt 1):051903. PubMed ID: 23214810
[TBL] [Abstract][Full Text] [Related]
17. Robust spike-train learning in spike-event based weight update.
Shrestha SB; Song Q
Neural Netw; 2017 Dec; 96():33-46. PubMed ID: 28957730
[TBL] [Abstract][Full Text] [Related]
18. Multiscale analysis of neural spike trains.
Ramezan R; Marriott P; Chenouri S
Stat Med; 2014 Jan; 33(2):238-56. PubMed ID: 23996238
[TBL] [Abstract][Full Text] [Related]
19. Dynamics of neurons in the cat lateral geniculate nucleus: in vivo electrophysiology and computational modeling.
Mukherjee P; Kaplan E
J Neurophysiol; 1995 Sep; 74(3):1222-43. PubMed ID: 7500146
[TBL] [Abstract][Full Text] [Related]
20. Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS).
Ahmadi N; Constandinou TG; Bouganis CS
PLoS One; 2018; 13(11):e0206794. PubMed ID: 30462665
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
