Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

October 2006, Vol. 18, No. 10, Pages 2387-2413
(doi: 10.1162/neco.2006.18.10.2387)
© 2006 Massachusetts Institute of Technology
Spatiotemporal Structure in Large Neuronal Networks Detected from Cross-Correlation
Article PDF (302.32 KB)
Abstract

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.