Local convergence analysis of a grouped variable version of coordinate descent
Journal of Optimization Theory and Applications
Learning in graphical models
Synchronous firing and higher-order interactions in neuron pool
Neural Computation
Some Notes on Alternating Optimization
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Neural Computation
Bifurcations in Morris-Lecar neuron model
Neurocomputing
On the synchrony of morphological and molecular signaling events in cell migration
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
On similarity measures for spike trains
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Multivariate autoregressive modeling and granger causality analysis of multiple spike trains
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Quantifying statistical interdependence, part iii: N 2 point processes
Neural Computation
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We present a novel approach to quantify the statistical interdependence of two time series, referred to as stochastic event synchrony (SES). The first step is to extract “events” from the two given time series. The next step is to try to align events from one time series with events from the other. The better the alignment, the more similar the two time series are considered to be. More precisely, the similarity is quantified by the following parameters: time delay, variance of the timing jitter, fraction of noncoincident events, and average similarity of the aligned events. The pairwise alignment and SES parameters are determined by statistical inference. In particular, the SES parameters are computed by maximum a posteriori (MAP) estimation, and the pairwise alignment is obtained by applying the max-product algorithm. This letter deals with one-dimensional point processes; the extension to multidimensional point processes is considered in a companion letter in this issue. By analyzing surrogate data, we demonstrate that SES is able to quantify both timing precision and event reliability more robustly than classical measures can. As an illustration, neuronal spike data generated by the Morris-Lecar neuron model are considered.