title: A method for decoding the neurophysiological spike-response transform.
abstract: Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.
authors.affiliation: Fishberg Department of Neuroscience, Mount Sinai School of Medicine, Box 1065, One Gustave L. Levy Place, New York, NY 10029, United States. estee.stern@mssm.edu
authors.forename: Estee
authors.initials: E
authors.lastname: Stern
authors.forename: Keyla
authors.initials: K
authors.lastname: García-Crescioni
authors.forename: Mark W
authors.initials: MW
authors.lastname: Miller
authors.forename: Charles S
authors.initials: CS
authors.lastname: Peskin
authors.forename: Vladimir
authors.initials: V
authors.lastname: Brezina
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D002490:Central Nervous System / Q000502:physiology*; D003198:Computer Simulation; D004594:Electrophysiology / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D008432:Mathematical Computing; D009469:Neuromuscular Junction / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D009482:Neurophysiology / Q000379:methods*; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D009435:Synaptic Transmission / Q000502:physiology
references.citation: Neuron. 2003 Oct 9;40(2):331-46
references.pmid: 14556712
references.citation: Annu Rev Physiol. 1999;61:521-42
references.pmid: 10099700
references.citation: J Neurosci. 2005 Nov 23;25(47):11003-13
references.pmid: 16306413
references.citation: Biol Cybern. 1986;55(2-3):135-44
references.pmid: 3801534
references.citation: J Neurophysiol. 2007 Nov;98(5):2887-902
references.pmid: 17804580
references.citation: Annu Rev Physiol. 2002;64:355-405
references.pmid: 11826273
references.citation: Biol Cybern. 1986;55(2-3):125-34
references.pmid: 3801533
references.citation: J Neurosci. 1996 Oct 1;16(19):6307-18
references.pmid: 8815910
references.citation: J Neurophysiol. 2007 May;97(5):3407-20
references.pmid: 17303813
references.citation: J Gen Physiol. 1977 Aug;70(2):243-65
references.pmid: 197201
references.citation: Ann Biomed Eng. 1991;19(4):429-55
references.pmid: 1741525
references.citation: J Gen Physiol. 1982 Oct;80(4):613-38
references.pmid: 6128373
references.citation: Neurocomputing (Amst). 2007 Jun 1;70(10):1753-1758
references.pmid: 19763188
references.citation: Biol Bull. 2002 Apr;202(2):108-36
references.pmid: 11971808
references.citation: J Comput Neurosci. 2009 Feb;26(1):1-19
references.pmid: 18506609
references.citation: J Neurophysiol. 2003 Apr;89(4):1815-25
references.pmid: 12611948
references.citation: Physiology (Bethesda). 2006 Apr;21:86-92
references.pmid: 16565474
references.citation: J Neurophysiol. 2003 Mar;89(3):1363-77
references.pmid: 12626616
references.citation: Biol Cybern. 1988;58(5):313-20
references.pmid: 3382702
references.citation: J Neurophysiol. 2000 Jan;83(1):207-31
references.pmid: 10634868
references.citation: Neuron. 2000 Jul;27(1):25-32
references.pmid: 10939328
references.citation: J Neurosci. 2001 Aug 1;21(15):5781-93
references.pmid: 11466450
references.citation: J Gen Physiol. 1982 Oct;80(4):583-611
references.pmid: 6128372
references.citation: J Neurophysiol. 2004 Dec;92(6):3455-70
references.pmid: 15295014
references.citation: Physiol Rev. 1992 Apr;72(2):491-505
references.pmid: 1557430
references.citation: J Neurosci Methods. 2009 Nov 15;184(2):337-56
references.pmid: 19695289
references.citation: J Comput Neurosci. 2009 Feb;26(1):21-37
references.pmid: 18504530
references.citation: J Physiol. 1975 Feb;245(1):183-208
references.pmid: 165286
references.citation: J Neurophysiol. 1973 Jul;36(4):605-18
references.pmid: 4713310