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      Methods for identification of spike patterns in massively parallel spike trains

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          Temporally, precise correlations between simultaneously recorded neurons have been interpreted as signatures of cell assemblies, i.e., groups of neurons that form processing units. Evidence for this hypothesis was found on the level of pairwise correlations in simultaneous recordings of few neurons. Increasing the number of simultaneously recorded neurons increases the chances to detect cell assembly activity due to the larger sample size. Recent technological advances have enabled the recording of 100 or more neurons in parallel. However, these massively parallel spike train data require novel statistical tools to be analyzed for correlations, because they raise considerable combinatorial and multiple testing issues. Recently, various of such methods have started to develop. First approaches were based on population or pairwise measures of synchronization, and later led to methods for the detection of various types of higher-order synchronization and of spatio-temporal patterns. The latest techniques combine data mining with analysis of statistical significance. Here, we give a comparative overview of these methods, of their assumptions and of the types of correlations they can detect.

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          The online version of this article (10.1007/s00422-018-0755-0) contains supplementary material, which is available to authorized users.

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          Most cited references 94

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          Information Theory and Statistical Mechanics

           E. T. Jaynes (1957)
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            A mechanism for cognitive dynamics: neuronal communication through neuronal coherence.

             Pascal Fries (2005)
            At any one moment, many neuronal groups in our brain are active. Microelectrode recordings have characterized the activation of single neurons and fMRI has unveiled brain-wide activation patterns. Now it is time to understand how the many active neuronal groups interact with each other and how their communication is flexibly modulated to bring about our cognitive dynamics. I hypothesize that neuronal communication is mechanistically subserved by neuronal coherence. Activated neuronal groups oscillate and thereby undergo rhythmic excitability fluctuations that produce temporal windows for communication. Only coherently oscillating neuronal groups can interact effectively, because their communication windows for input and for output are open at the same times. Thus, a flexible pattern of coherence defines a flexible communication structure, which subserves our cognitive flexibility.
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              Large-scale recording of neuronal ensembles.

              How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron-electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.

                Author and article information

                Biol Cybern
                Biol Cybern
                Biological Cybernetics
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                12 April 2018
                12 April 2018
                : 112
                : 1
                : 57-80
                [1 ]ISNI 0000 0001 2297 375X, GRID grid.8385.6, Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure-Function Relationships (INM-10), , Jülich Research Centre, ; Jülich, Germany
                [2 ]ISNI 0000 0000 8580 3777, GRID grid.6190.e, Computational Systems Neuroscience, Institute for Zoology, Faculty of Mathematics and Natural Sciences, , University of Cologne, ; Cologne, Germany
                [3 ]Chair of Risk, Safety and Uncertainty Quantification, ETH Zürich, Zurich, Switzerland
                [4 ]Risk Center, ETH Zürich, Zurich, Switzerland
                [5 ]ISNI 0000 0001 0728 696X, GRID grid.1957.a, Theoretical Systems Neurobiology, , RWTH Aachen University, ; Aachen, Germany
                Author notes

                Communicated by Martin Paul Nawrot.

                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                Funded by: Helmholtz-Gemeinschaft (DE)
                Award ID: SMHB
                Award Recipient :
                Funded by: EC Human Brain Project, HBP
                Award ID: 720270
                Award Recipient :
                Funded by: DFG SPP priority Program 1665
                Award ID: GR 1753/4-2
                Award Recipient :
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                © Springer-Verlag GmbH Germany, part of Springer Nature 2018


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