Multivariate autoregressive modeling and granger causality analysis of multiple spike trains.
Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ''hidden" Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method.
Krumin
Michael
M
Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, 32000 Haifa, Israel.
Shoham
Shy
S
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D001331:Automation; D003198:Computer Simulation; D016014:Linear Models; D008959:Models, Neurological; D015233:Models, Statistical; D015999:Multivariate Analysis; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D016012:Poisson Distribution; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted
Prog Neurobiol. 2005 Sep-Oct;77(1-2):1-37
16289760
Proc Natl Acad Sci U S A. 2001 Dec 18;98(26):15330-5
11752471
J Neurosci. 2004 Sep 29;24(39):8551-61
15456829
Phys Rev Lett. 2010 Feb 5;104(5):058102
20366796
Biol Cybern. 1985;52(5):301-6
2996632
Neural Comput. 2002 Oct;14(10):2269-316
12396564
Neural Comput. 2006 Feb;18(2):245-82
16378515
J Neurophysiol. 2005 Feb;93(2):1074-89
15356183
Nature. 2008 Aug 21;454(7207):995-9
18650810
Neurocomputing. 2003 Jun 1;52-54:925-931
20740049
Neural Comput. 2001 Apr;13(4):751-63
11255567
Biol Cybern. 1988;59(4-5):265-75
3196770
Hum Brain Mapp. 2007 Feb;28(2):143-57
16761264
Nat Neurosci. 2004 May;7(5):456-61
15114358
Physiology (Bethesda). 2007 Dec;22:358-65
18073408
Biol Cybern. 2003 May;88(5):352-9
12750897
J Comput Neurosci. 2010 Aug;29(1-2):301-308
19757006
Neural Comput. 2009 Jun;21(6):1642-64
19191596
Biophys J. 1990 May;57(5):987-99
2340346
Neural Comput. 2009 Aug;21(8):2152-202
19670479
Biol Cybern. 2001 Aug;85(2):145-57
11508777
J Neurophysiol. 2008 Oct;100(4):2165-76
18632888
Biol Cybern. 2006 Oct;95(4):349-69
16927098
Curr Opin Neurobiol. 2005 Oct;15(5):585-92
16140522
Neural Comput. 2005 Sep;17(9):1927-61
15992486
Kybernetik. 1970 Nov;7(5):167-74
4324856
J Neurophysiol. 2003 Jul;90(1):387-94
12634276
Biol Cybern. 1986;55(4):219-25
3801543
J Comput Neurosci. 2004 Jul-Aug;17(1):7-11
15218350
Nat Methods. 2005 Nov;2(11):837-43
16278654
J Neurosci Methods. 1999 Dec 15;94(1):93-103
10638818
Neural Comput. 2003 Oct;15(10):2359-77
14511525
Neural Comput. 2009 Feb;21(2):397-423
19196233
J Comput Neurosci. 2009 Aug;27(1):55-64
19137420
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:6368-70
18003478
IEEE Trans Neural Netw. 2003;14(6):1569-72
18244602
Neural Comput. 2000 Nov;12(11):2621-53
11110130