PMID- 28025784
OWN - NLM
STAT- MEDLINE
DCOM- 20180507
LR  - 20181202
IS  - 1573-6873 (Electronic)
IS  - 0929-5313 (Linking)
VI  - 42
IP  - 2
DP  - 2017 Apr
TI  - Multi-scale detection of rate changes in spike trains with weak dependencies.
PG  - 187-201
LID - 10.1007/s10827-016-0635-3 [doi]
AB  - The statistical analysis of neuronal spike trains by models of point processes 
      often relies on the assumption of constant process parameters. However, it is a 
      well-known problem that the parameters of empirical spike trains can be highly 
      variable, such as for example the firing rate. In order to test the null 
      hypothesis of a constant rate and to estimate the change points, a Multiple 
      Filter Test (MFT) and a corresponding algorithm (MFA) have been proposed that can 
      be applied under the assumption of independent inter spike intervals (ISIs). As 
      empirical spike trains often show weak dependencies in the correlation structure 
      of ISIs, we extend the MFT here to point processes associated with short range 
      dependencies. By specifically estimating serial dependencies in the test 
      statistic, we show that the new MFT can be applied to a variety of empirical 
      firing patterns, including positive and negative serial correlations as well as 
      tonic and bursty firing. The new MFT is applied to a data set of empirical spike 
      trains with serial correlations, and simulations show improved performance 
      against methods that assume independence. In case of positive correlations, our 
      new MFT is necessary to reduce the number of false positives, which can be highly 
      enhanced when falsely assuming independence. For the frequent case of negative 
      correlations, the new MFT shows an improved detection probability of change 
      points and thus, also a higher potential of signal extraction from noisy spike 
      trains.
FAU - Messer, Michael
AU  - Messer M
AD  - Institute of Mathematics, Johann Wolfgang Goethe University Frankfurt, Frankfurt, 
      Germany.
FAU - Costa, Kaue M
AU  - Costa KM
AD  - Institute of Neurophysiology, Johann Wolfgang Goethe University Frankfurt, 
      Frankfurt, Germany.
FAU - Roeper, Jochen
AU  - Roeper J
AD  - Institute of Neurophysiology, Johann Wolfgang Goethe University Frankfurt, 
      Frankfurt, Germany.
FAU - Schneider, Gaby
AU  - Schneider G
AUID- ORCID: 0000-0001-5791-6405
AD  - Institute of Mathematics, Johann Wolfgang Goethe University Frankfurt, Frankfurt, 
      Germany. schneider@math.uni-frankfurt.de.
LA  - eng
PT  - Journal Article
DEP - 20161226
PL  - United States
TA  - J Comput Neurosci
JT  - Journal of computational neuroscience
JID - 9439510
SB  - IM
MH  - *Action Potentials
MH  - Algorithms
MH  - Humans
MH  - *Models, Neurological
MH  - Neurons/physiology
MH  - Probability
OTO - NOTNLM
OT  - Change point detection
OT  - Multi scale
OT  - Non-stationarity
OT  - Point processes
OT  - Serial correlation
OT  - Spike train analysis
EDAT- 2016/12/28 06:00
MHDA- 2018/05/08 06:00
CRDT- 2016/12/28 06:00
PHST- 2016/07/17 00:00 [received]
PHST- 2016/12/07 00:00 [accepted]
PHST- 2016/11/23 00:00 [revised]
PHST- 2016/12/28 06:00 [pubmed]
PHST- 2018/05/08 06:00 [medline]
PHST- 2016/12/28 06:00 [entrez]
AID - 10.1007/s10827-016-0635-3 [pii]
AID - 10.1007/s10827-016-0635-3 [doi]
PST - ppublish
SO  - J Comput Neurosci. 2017 Apr;42(2):187-201. doi: 10.1007/s10827-016-0635-3. Epub 
      2016 Dec 26.