Exploring the Usefulness of Formal Concept Analysis for Robust Detection of Spatio-temporal Spike Patterns in Massively Parallel Spike Trains

Yegenoglu, Alper; Quaglio, Pietro; Torre, Emiliano; Grün, Sonja; Endres, Dominik

doi:10.1007/978-3-319-40985-6_1

Cited by 10 publications

(24 citation statements)

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“…SPADE builds on and brings together two techniques that we had previously introduced for the identification of STPs in massively parallel spike trains (Yegenoglu et al, 2016) and for the statistical evaluation of patterns of synchronous spikes (Torre et al, 2013). The latter avoided the computational and multiple testing issues that usually prevent applying such analyses to large data sets.…”

Section: Discussionmentioning

confidence: 99%

“…The attributes are the spike time-indexes relative to the window start, combined with the neuron identities. The figure is adapted from Yegenoglu et al ( 2016 ).…”

Section: Methodsmentioning

confidence: 99%

“…Due to the temporal dimension taken into account here, the analysis of STPs has to deal with a much (possibly orders of magnitude) larger number of possible patterns compared to the analysis of spike synchrony. In Yegenoglu et al ( 2016 ), we addressed this problem by a new approach based on Formal Concept Analysis (FCA; see Ganter and Wille, 1999 ) to efficiently find STPs in MPST data, count their occurrences, and evaluate their stability (see Kuznetsov, 2007 ) as a measure for non-randomness. We successfully applied the method to artificial test data of up to 50 parallel spike trains with an average firing rate of about 15 Hz each simulated for 1 s. The analysis was computationally too expensive to be applied on larger data sets.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we improve the methodology introduced in Yegenoglu et al ( 2016 ) in three respects. First, we use frequent item set mining on a suitably restructured format of the data as an equivalent but computationally more efficient alternative to currently available FCA algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Section 2 presents the various steps of our novel method and links them to previous methods, mainly to the work we presented in Yegenoglu et al ( 2016 ) and Torre et al ( 2013 ). Section 3 compares the performance of the stability-based and significance-based (SPADE) approaches for patterns filtering, and provides selection criteria for candidate patterns.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

Quaglio

Yegenoglu

Torre

et al. 2017

Front. Comput. Neurosci.

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Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis.

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Section: Discussionmentioning

confidence: 99%

“…The attributes are the spike time-indexes relative to the window start, combined with the neuron identities. The figure is adapted from Yegenoglu et al ( 2016 ).…”

Section: Methodsmentioning

confidence: 99%