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Items: 1 to 20 of 136

1.

Modelling spike trains and extracting response latency with Bayesian binning.

Endres D, Schindelin J, Földiák P, Oram MW.

J Physiol Paris. 2010 May-Sep;104(3-4):128-36. doi: 10.1016/j.jphysparis.2009.11.015. Epub 2009 Nov 27.

PMID:
19945532
2.

Feature extraction from spike trains with Bayesian binning: 'latency is where the signal starts'.

Endres D, Oram M.

J Comput Neurosci. 2010 Aug;29(1-2):149-69. doi: 10.1007/s10827-009-0157-3. Epub 2009 May 16.

PMID:
19449095
3.

Trial-by-trial estimation of amplitude and latency variability in neuronal spike trains.

Bollimunta A, Knuth KH, Ding M.

J Neurosci Methods. 2007 Feb 15;160(1):163-70. Epub 2006 Sep 25.

PMID:
17000007
4.
5.

Bayesian estimation of stimulus responses in Poisson spike trains.

Lehky SR.

Neural Comput. 2004 Jul;16(7):1325-43.

PMID:
15165392
6.

Statistical smoothing of neuronal data.

Kass RE, Ventura V, Cai C.

Network. 2003 Feb;14(1):5-15.

PMID:
12613549
7.

First-spike latency in the presence of spontaneous activity.

Pawlas Z, Klebanov LB, Benes V, Prokesová M, Popelár J, Lánský P.

Neural Comput. 2010 Jul;22(7):1675-97. doi: 10.1162/neco.2010.11-09-1118.

PMID:
20235823
8.

Optimizing time histograms for non-Poissonian spike trains.

Omi T, Shinomoto S.

Neural Comput. 2011 Dec;23(12):3125-44. doi: 10.1162/NECO_a_00213. Epub 2011 Sep 15.

PMID:
21919781
9.

Functional identification of the input-output transforms of motoneurones in the rat and cat.

Poliakov AV, Powers RK, Binder MD.

J Physiol. 1997 Oct 15;504 ( Pt 2):401-24.

10.

Single-trial estimation of neuronal firing rates: from single-neuron spike trains to population activity.

Nawrot M, Aertsen A, Rotter S.

J Neurosci Methods. 1999 Dec 15;94(1):81-92.

PMID:
10638817
11.

An improved method for the estimation of firing rate dynamics using an optimal digital filter.

Cherif S, Cullen KE, Galiana HL.

J Neurosci Methods. 2008 Aug 15;173(1):165-81. doi: 10.1016/j.jneumeth.2008.05.021. Epub 2008 Jun 3.

PMID:
18577401
12.

Elimination of response latency variability in neuronal spike trains.

Nawrot MP, Aertsen A, Rotter S.

Biol Cybern. 2003 May;88(5):321-34.

PMID:
12750895
13.

Phase-response curves give the responses of neurons to transient inputs.

Gutkin BS, Ermentrout GB, Reyes AD.

J Neurophysiol. 2005 Aug;94(2):1623-35. Epub 2005 Apr 13.

14.

Applications of the spike density function in analysis of neuronal firing patterns.

Szucs A.

J Neurosci Methods. 1998 Jun 1;81(1-2):159-67.

PMID:
9696321
15.

Attractor reliability reveals deterministic structure in neuronal spike trains.

Tiesinga PH, Fellous JM, Sejnowski TJ.

Neural Comput. 2002 Jul;14(7):1629-50.

PMID:
12079549
16.

Trial-to-trial variability and its effect on time-varying dependency between two neurons.

Ventura V, Cai C, Kass RE.

J Neurophysiol. 2005 Oct;94(4):2928-39.

18.

A simple indicator of nonstationarity of firing rate in spike trains.

Gourévitch B, Eggermont JJ.

J Neurosci Methods. 2007 Jun 15;163(1):181-7. Epub 2007 Mar 3.

PMID:
17418899
20.

Estimating receptive fields in the presence of spike-time jitter.

Gollisch T.

Network. 2006 Jun;17(2):103-29.

PMID:
16818393

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