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

View ORCID ProfileRubén Herzog, View ORCID ProfileArturo Morales, Soraya Mora, Joaquin Araya, View ORCID ProfileMaría-José Escobar, Adrian G. Palacios, View ORCID ProfileRodrigo Cofré
doi: https://doi.org/10.1101/2020.10.12.335901
Rubén Herzog
1Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
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  • For correspondence: rubenherzog@ug.uchile.cl
Arturo Morales
2Universidad Técnica Federico Santa María, Departamento de Electrónica, Valparaíso, Chile
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Soraya Mora
3Facultad de Medicina y Ciencia, Universidad San Sebastián, Santiago, Chile
4Laboratorio de Biología Computacional, Fundación Ciencia y Vida, Santiago, Chile
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Joaquin Araya
1Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
5Escuela de Tecnología Médica, Facultad de Salud, Universidad Santo Tomás, Santiago, Chile
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María-José Escobar
2Universidad Técnica Federico Santa María, Departamento de Electrónica, Valparaíso, Chile
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Adrian G. Palacios
1Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
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Rodrigo Cofré
6CIMFAV Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile
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  • ORCID record for Rodrigo Cofré
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Abstract

We propose a novel, scalable, and accurate automated method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. 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 is already organized in clearly distinguishable functional ensembles. 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. Additionally, we found that our method outperforms current alternative methodologies. Finally, we provide a Graphic User Interface, which aims to facilitate our method’s use by the scientific community.

Author summary Neuronal ensembles are strongly interconnected groups of neurons that tend to fire together (Hebb 1949). However, even when this concept was proposed more than 70 years ago, only recent advances in multi-electrode arrays and calcium imaging, statistical methods, and computing power have made it possible to record and analyze multiple neurons’ activities spiking simultaneously, providing a unique opportunity to study how groups of neurons form ensembles spontaneously and under different stimuli scenarios. Using our method, we found that retinal ganglion cells show a consistent stimuli-evoked ensemble activity, and, when validated with synthetic data, the method shows good performance by detecting the number of ensembles, the activation times, and the core-cells for a wide range of firing rates and number of ensembles accurately.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 13, 2020.
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Scalable and accurate automated method for neuronal ensemble detection in spiking neural networks
Rubén Herzog, Arturo Morales, Soraya Mora, Joaquin Araya, María-José Escobar, Adrian G. Palacios, Rodrigo Cofré
bioRxiv 2020.10.12.335901; doi: https://doi.org/10.1101/2020.10.12.335901
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Scalable and accurate automated method for neuronal ensemble detection in spiking neural networks
Rubén Herzog, Arturo Morales, Soraya Mora, Joaquin Araya, María-José Escobar, Adrian G. Palacios, Rodrigo Cofré
bioRxiv 2020.10.12.335901; doi: https://doi.org/10.1101/2020.10.12.335901

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