Determining Burst Firing Time Distributions from Multiple Spike Trains

Recent experimental findings have shown the presence of robust and cell-type-specific intraburst firing patterns in bursting neurons. We address the problem of characterizing these patterns under the assumption that the bursts exhibit well-defined firing time distributions. We propose a method for e...

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Bibliographic Details
Published in:Neural computation 2009-04, Vol.21 (4), p.973-990
Main Authors: Lago-Fernández, Luis F., Szücs, Attila, Varona, Pablo
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Algorithms
Animals
Applied sciences
Artificial intelligence
Biological and medical sciences
Computer science
control theory
systems
Computer Simulation
Exact sciences and technology
Experiments
Fundamental and applied biological sciences. Psychology
Ganglia, Invertebrate - cytology
Ganglia, Invertebrate - physiology
General aspects
Homarus americanus
Learning and adaptive systems
Letters
Mathematics
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Methods
Miscellaneous
Models, Neurological
Multivariate analysis
Nephropidae
Neurons
Neurons - physiology
Probability and statistics
Sciences and techniques of general use
Simulation
Statistics
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Summary:Recent experimental findings have shown the presence of robust and cell-type-specific intraburst firing patterns in bursting neurons. We address the problem of characterizing these patterns under the assumption that the bursts exhibit well-defined firing time distributions. We propose a method for estimating these distributions based on a burst alignment algorithm that minimizes the overlap among the firing time distributions of the different spikes within the burst. This method provides a good approximation to the burst's intrinsic temporal structure as a set of firing time distributions. In addition, the method allows labeling the spikes in any particular burst, establishing a correspondence between each spike and the distribution that best explains it, and identifying missing spikes. Our results on both simulated and experimental data from the lobster stomatogastric ganglion show that the proposed method provides a reliable characterization of the intraburst firing patterns and avoids the errors derived from missing spikes. This method can also be applied to nonbursting neurons as a general tool for the study and the interpretation of firing time distributions as part of a temporal neural code.
ISSN:0899-7667
1530-888X
DOI:10.1162/neco.2008.07-07-571