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000185738 037__ $$aFZJ-2014-07163
000185738 041__ $$aEnglish
000185738 1001_ $$0P:(DE-Juel1)159172$$aCanova, Carlos$$b0$$eCorresponding Author$$gmale$$ufzj
000185738 245__ $$aStatistical Assessment and Neuronal Composition of Active Synfire Chains$$f2014-09-01
000185738 260__ $$c2014
000185738 300__ $$a75
000185738 3367_ $$0PUB:(DE-HGF)10$$2PUB:(DE-HGF)$$aDiploma Thesis$$bdiploma$$mdiploma$$s1429533752_28968
000185738 3367_ $$02$$2EndNote$$aThesis
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000185738 3367_ $$2DRIVER$$amasterThesis
000185738 3367_ $$2ORCID$$aSUPERVISED_STUDENT_PUBLICATION
000185738 3367_ $$2BibTeX$$aMASTERSTHESIS
000185738 502__ $$aUniversität Tübingen, Diplomarbeit, 2014$$bDipl.$$cUniversität Tübingen$$d2014
000185738 500__ $$abitte dem korrekten Teilprojekt von : SPP 1665: Aufschlüsselung und Manipulation neuronaler Netzwerke im Gehirn von Säugetieren: Von korrelativen zur kausalen Analyse  zuordnen
000185738 520__ $$aThe synfire chain (SFC) model has been suggested (Abeles, 1982, 1991) as a network model for cortical cell assemblies (Hebb, 1949). It is composed of consecutive groups of neurons, where each group is connected to the next in a feedforward fashion by a large number of convergent and divergent inputs. This connectivity structure enables stable propagation of packets of synchronous spiking activity through the network after stimulation of the first group (Diesmann et al., 1999a). Recent advances in electrophysiological recording techniques enable to record one hundred or more individual neurons simultaneously, thereby increasing the chance to detect active cell assemblies. Schrader et al. (2008) and Gerstein et al. (2012) suggested a method based on an intersection matrix to detect active SFCs. Each entry in the matrix contains the degree of overlap of identical neurons being active at two different time bins. If a particular SFC isa ctivated twice, a diagonal structure, composed of consecutive time bins of high intersection values, appears in the matrix. Further convolution of the matrix with a diagonal linear filter enhances diagonal structures as a whole compared to isolated high intersection values due to chance overlaps, allowing to distinguish among the two. Several features of the data are expected to interfere with the analysis. First, the contrast between diagonal structures and the rest of the matrix can be severely diminished by high firing rates of the neurons, thereby increasing the intersection values of individual background pixels. Second, undersampling of the system and/or stochastic participation in assembly activation may lead to diagonal structures that fluctuate in intensity or even become discontinuous. Third, diagonal structures may become wiggly due to interference of the analysis bin width and the propagation speed of the SFC. This thesis introduces a quantitative statistical analysis method for the presence of SFCsbuilt on the original approach which features an automatic identification of the neurons participating in the SFC. In the first stage, six steps are performed in addition to the construction of the original intersection matrix (step 1 in Figure 6.1). Diagonal structures are enhanced by convolving the intersection matrix with a block filter, which enables to cope with wiggly structures (step 2). Then, a statistical test is performed to assess if there are significant sequences of high intersection values (step 3) by generating multiple realizations of surrogate matrices. In these surrogates, the positions of the original matrix entries are randomized before filtering, thereby implementing the null hypothesis of repeated synchronous activations of specific neuronal groups but not in a consecutive manner in time. The entries of all surrogate matrices form thedistribution of matrix entries under the stated null hypothesis. By choosing an upper quantile (e.g. 0.1%), a threshold is defined to identify statistically significant entries (step 3). The resulting matrix is converted into a mask of regions of interest (step 4), which is then applied to the original intersection matrix (step 5). A second threshold is calculated from a further set of surrogate matrices which are generated by dithering the spike times, in order to implement a null hypotheses of no spike synchrony in the data. The masked intersection matrix is thresholded with this new significance level, yielding the final matrix in step 6. Ideally, this final matrix contains only significant entries which are the signature of repeated SFC activity. The second stage of the method characterizes the significant entries from the final matrix. Using a clustering algorithm on those entries, the diagonal structures are labeled as repetitions of an active synfire chains (step 7). The IDs of the neurons participating in the chain(s) are then extracted to identify the neurons composing the SFC and their group membership (step 8). The method was calibrated using stochastic simulations consisting of repeating consecutive synchronous spike patterns embedded in otherwise independent data. Performance results from the calibrations show that in realistic parameter regimes, e.g. with realistic spike rates and downsampled networks, the new analysis method is able to successfully identify large portions (> 90%) of the embedded SFCs while having low false positive and false negative levels. Possible applications to electrophysiological data were demonstrated, identifying where the method has to be improved for practical use.
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