ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Neural Signatures: Multiple Coding in Spiking–bursting Cells
Biological Cybernetics
Spike alignment in bursting neurons
Neurocomputing
Interacting slow and fast dynamics in precise spiking-bursting neurons
IWINAC'05 Proceedings of the First international conference on Mechanisms, Symbols, and Models Underlying Cognition: interplay between natural and artificial computation - Volume Part I
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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.