Scalable and accurate method for neuronal ensemble detection in spiking neural networks

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2021

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item.page.issne

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Centro Interdisciplinario de Neurociencia de Valparaiso

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Resumen

We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. We found that our method outperforms current alternative methodologies. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity could be organized in distinguishable functional ensembles. We provide a Graphic User Interface, which facilitates the use of our method by the scientific community.

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NEURONS, RETINAL GANGLION CELLS, PRINCIPAL COMPONENT ANALYSIS, NEURONAL TUNING, ACTION POTENTIALS, PROBABILITY DENSITY, ALGORITHMS, VISION

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Copyright: © 2021 Herzog et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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