An information-geometric framework for statistical inferences in the neural spike train space
Journal of Computational Neuroscience
Improved similarity measures for small sets of spike trains
Neural Computation
Strictly positive-definite spike train kernels for point-process divergences
Neural Computation
Random bin for analyzing neuron spike trains
Computational Intelligence and Neuroscience - Special issue on Computational Intelligence in Biomedical Science and Engineering
Characterisation of information flow in an izhikevich network
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Discovering the multi-neuronal firing patterns based on a new binless spike trains measure
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Reliability of spike and burst firing in thalamocortical relay cells
Journal of Computational Neuroscience
Synergy, redundancy, and multivariate information measures: an experimentalist's perspective
Journal of Computational Neuroscience
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Several binless spike train measures which avoid the limitations of binning have been recently been proposed in the literature. This paper presents a systematic comparison of these measures in three simulated paradigms designed to address specific situations of interest in spike train analysis where the relevant feature may be in the form of firing rate, firing rate modulations, and/or synchrony. The measures are first disseminated and extended for ease of comparison. It also discusses how the measures can be used to measure dissimilarity in spike trains' firing rate despite their explicit formulation for synchrony.