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Machine classification of spatiotemporal patterns: automated parameter search in a rebounding spiking network

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Abstract

Various patterns of electrical activities, including travelling waves, have been observed in cortical experimental data from animal models as well as humans. By applying machine learning techniques, we investigate the spatiotemporal patterns, found in a spiking neuronal network with inhibition-induced firing (rebounding). Our cortical sheet model produces a wide variety of network activities including synchrony, target waves, and travelling wavelets. Pattern formation is controlled by modifying a Gaussian derivative coupling kernel through varying the level of inhibition, coupling strength, and kernel geometry. We have designed a computationally efficient machine classifier, based on statistical, textural, and temporal features, to identify the parameter regimes associated with different spatiotemporal patterns. Our results reveal that switching between synchrony and travelling waves can occur transiently and spontaneously without a stimulus, in a noise-dependent fashion, or in the presence of stimulus when the coupling strength and level of inhibition are at moderate values. They also demonstrate that when a target wave is formed, its wave speed is most sensitive to perturbations in the coupling strength between model neurons. This study provides an automated method to characterize activities produced by a novel spiking network that phenomenologically models large scale dynamics in the cortex.

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Acknowledgements

This work was supported by a Natural Sciences and Engineering Council of Canada discovery grant to A.K, and by Chercheur-boursier de merite grant to C.P.

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  1. Department of Physiology, McGill University, Montréal, QC, Canada

    Lawrence Oprea & Anmar Khadra

  2. Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada

    Christopher C. Pack

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  1. Lawrence Oprea
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Correspondence to Anmar Khadra.

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Oprea, L., Pack, C.C. & Khadra, A. Machine classification of spatiotemporal patterns: automated parameter search in a rebounding spiking network. Cogn Neurodyn 14, 267–280 (2020). https://doi.org/10.1007/s11571-020-09568-8

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