dc.creator | Domínguez Morales, Juan Pedro | es |
dc.creator | Buccelli, Stefano | es |
dc.creator | Gutiérrez Galán, Daniel | es |
dc.creator | Colombi, Ilaria | es |
dc.creator | Jiménez Fernández, Ángel Francisco | es |
dc.creator | Chiappalone, Michela | es |
dc.date.accessioned | 2021-09-14T07:31:16Z | |
dc.date.available | 2021-09-14T07:31:16Z | |
dc.date.issued | 2021-08 | |
dc.identifier.citation | Domínguez Morales, J.P., Buccelli, S., Gutiérrez Galán, D., Colombi, I., Jiménez Fernández, Á.F. y Chiappalone, M. (2021). Real-time detection of bursts in neuronal cultures using a Neuromorphic Auditory Sensor and Spiking Neural Networks. Neurocomputing, 449, 422-434. | |
dc.identifier.issn | 0925-2312 | es |
dc.identifier.uri | https://hdl.handle.net/11441/125691 | |
dc.description.abstract | The correct identi cation of burst events is crucial in many scenarios,
ranging from basic neuroscience to biomedical applications. However, none
of the burst detection methods that can be found in the literature have been
widely adopted for this task. As an alternative to conventional techniques, a
novel neuromorphic approach for real-time burst detection is proposed and
tested on acquisitions from in vitro cultures. The system consists of a Neuromorphic
Auditory Sensor, which converts the input signal obtained from
electrophysiological recordings into spikes and decomposes them into di erent
frequency bands. The output of the sensor is sent to a trained spiking
neural network implemented on a SpiNNaker board that discerns between
bursting and non-bursting activity. This data-driven approach was compared
with 8 di erent conventional spike-based methods, addressing some of
their drawbacks, such as being able to detect both high and low frequency
events and working in an online manner. Similar results in terms of number
of detected events, mean burst duration and correlation as current state-ofthe-
art approaches were obtained with the proposed system, also bene ting from its lower power consumption and computational latency. Therefore,
our neuromorphic-based burst detection paves the road to future implementations
for neuroprosthetic applications. | es |
dc.description.sponsorship | Spanish Ministry of Education, Culture and Sport "Formación de Personal Universitario Scholarship" | es |
dc.description.sponsorship | European Regional Development Fund COFNET TEC2016-77785-P | es |
dc.format | application/pdf | es |
dc.format.extent | 34 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Neurocomputing, 449, 422-434. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | SpiNNaker | es |
dc.subject | Spiking Neural Networks | es |
dc.subject | Neuromorphic Hardware | es |
dc.subject | Brain Signals Processing | es |
dc.subject | Burst detection | es |
dc.title | Real-time detection of bursts in neuronal cultures using a Neuromorphic Auditory Sensor and Spiking Neural Networks | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.projectID | TEC2016-77785-P | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0925231221005014 | es |
dc.identifier.doi | 10.1016/j.neucom.2021.03.109 | es |
dc.contributor.group | Universidad de Sevilla. TEP108: Robótica y Tecnología de Computadores | es |
idus.validador.nota | Preprint. Submitted version
Mejor artículo del mes de agosto de 2021 en Escuela Politécnica Superior, Universidad de Sevilla
Awarded as a best scientific publication of the month of August-2021 in Escuela Politécnica Superior, University of Seville. | es |
dc.journaltitle | Neurocomputing | es |
dc.publication.volumen | 449 | es |
dc.publication.initialPage | 422 | es |
dc.publication.endPage | 434 | es |
dc.description.awardwinning | Premio Mensual Publicación Científica Destacada de la US. Escuela Politécnica Superior | |