Spatiotemporal Structure in Large Neuronal Networks Detected from Cross-Correlation

• Published in: Neural computation 2006-10, Vol.18 (10), p.2387-2413
• Main Authors: Schneider, Gaby, Havenith, Martha N, Nikolić, Danko
• Format: Article
• Language: English
• Subjects: Action Potentials - physiology; Animals; Applied sciences; Artificial intelligence; Biological and medical sciences; Cats; Computer science; control theory; systems; Connectionism. Neural networks; Correlation analysis; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects; Histograms; Learning and adaptive systems; Mathematics; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Neurological; Multivariate analysis; Nerve Net - physiology; Neural networks; Neurons; Neurons - physiology; Nonlinear Dynamics; Photic Stimulation - methods; Probability and statistics; Sciences and techniques of general use; Statistics; Statistics as Topic; Stochastic Processes; Time Factors; Visual Cortex - cytology; Visual Cortex - physiology
• ISSN: 0899-7667; 1530-888X
• DOI: 10.1162/neco.2006.18.10.2387

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.