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Spatiotemporal Structure in Large Neuronal Networks Detected from Cross-Correlation

gaby.schneider{at}math.uni-frankfurt.de Department of Computer Science and Mathematics, Johann Wolfgang Goethe University, Frankfurt (Main), Germany

Martha N. Havenith

havenith{at}mpih-frankfurt.mpg.de Department of Neurophysiology, Max-Planck-Institute for Brain Research, Frankfurt (Main), Germany

Danko Nikoli

danko{at}mpih-frankfurt.mpg.de Department of Neurophysiology, Max-Planck-Institute for Brain Research, Frankfurt (Main), and Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University, Frankfurt (Main), Germany

The analysis of neuronal information involves the detection of spatiotemporal relations between neuronal discharges. We propose a method that is based on the positions (phase offsets) of the central peaks obtained from pairwise cross-correlation histograms. Data complexity is reduced to a one-dimensional representation by using redundancies in the measured phase offsets such that each unit is assigned a "preferred firing time" relative to the other units in the group. We propose two procedures to examine the applicability of this method to experimental data sets. In addition, we propose methods that help the investigation of dynamical changes in the preferred firing times of the units. All methods are applied to a sample data set obtained from cat visual cortex.

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