Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview

The rapid expansion of information systems in all areas of society demands more powerful, efficient, and low-energy consumption computing systems. Neuromorphic engineering has emerged as a solution that attempts to mimic the brain to incorporate its capabilities to solve complex problems in a comput...

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Detalles Bibliográficos
Autores: Casanueva Morato, Daniel, Ayuso Martínez, Álvaro, Indiveri, Giacomo, Domínguez Morales, Juan Pedro, Jiménez Moreno, Gabriel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/163536
Acceso en línea:https://hdl.handle.net/11441/163536
https://doi.org/10.1002/aisy.202400282
Access Level:acceso abierto
Palabra clave:Analog sequential memory
DYNAP-SE
Hippocampus model
Neuromorphic engineering
Robustness analysis
Spiking neural networks
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spelling Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis OverviewCasanueva Morato, DanielAyuso Martínez, ÁlvaroIndiveri, GiacomoDomínguez Morales, Juan PedroJiménez Moreno, GabrielAnalog sequential memoryDYNAP-SEHippocampus modelNeuromorphic engineeringRobustness analysisSpiking neural networksThe rapid expansion of information systems in all areas of society demands more powerful, efficient, and low-energy consumption computing systems. Neuromorphic engineering has emerged as a solution that attempts to mimic the brain to incorporate its capabilities to solve complex problems in a computationally and energy-efficient way in real time. Within neuromorphic computing, building systems to efficiently store the information is still a challenge. Among all the brain regions, the hippocampus stands out as a short-term memory capable of learning and recalling large amounts of information quickly and efficiently. Herein, a spike-based bio-inspired hippocampus sequential memory model is proposed that makes use of the benefits of analog computing and spiking neural networks (SNNs): noise robustness, improved real-time operation, and energy efficiency. This model is applied to robotic navigation to learn and recall trajectories that lead to a goal position within a known grid environment. The model is implemented on the special-purpose SNNs mixed-signal DYNAP-SE hardware platform. Through extensive experimentation together with an extensive analysis of the model's behavior in the presence of external noise sources, its correct functioning is demonstrated, proving the robustness and consistency of the proposed neuromorphic sequential memory system.WileyArquitectura y Tecnología de ComputadoresTEP108: Robótica y Tecnología de ComputadoresMinisterio de Ciencia, Innovación y Universidades (MICINN). España2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/163536https://doi.org/10.1002/aisy.202400282reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésTED2021-130825B-I00PID2019-105556GB-C33PDC2023-145841-C33FPU20/01994PID2023-149071NB-C54https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202400282info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1635362026-06-17T12:51:07Z
dc.title.none.fl_str_mv Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview
title Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview
spellingShingle Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview
Casanueva Morato, Daniel
Analog sequential memory
DYNAP-SE
Hippocampus model
Neuromorphic engineering
Robustness analysis
Spiking neural networks
title_short Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview
title_full Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview
title_fullStr Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview
title_full_unstemmed Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview
title_sort Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview
dc.creator.none.fl_str_mv Casanueva Morato, Daniel
Ayuso Martínez, Álvaro
Indiveri, Giacomo
Domínguez Morales, Juan Pedro
Jiménez Moreno, Gabriel
author Casanueva Morato, Daniel
author_facet Casanueva Morato, Daniel
Ayuso Martínez, Álvaro
Indiveri, Giacomo
Domínguez Morales, Juan Pedro
Jiménez Moreno, Gabriel
author_role author
author2 Ayuso Martínez, Álvaro
Indiveri, Giacomo
Domínguez Morales, Juan Pedro
Jiménez Moreno, Gabriel
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
TEP108: Robótica y Tecnología de Computadores
Ministerio de Ciencia, Innovación y Universidades (MICINN). España
dc.subject.none.fl_str_mv Analog sequential memory
DYNAP-SE
Hippocampus model
Neuromorphic engineering
Robustness analysis
Spiking neural networks
topic Analog sequential memory
DYNAP-SE
Hippocampus model
Neuromorphic engineering
Robustness analysis
Spiking neural networks
description The rapid expansion of information systems in all areas of society demands more powerful, efficient, and low-energy consumption computing systems. Neuromorphic engineering has emerged as a solution that attempts to mimic the brain to incorporate its capabilities to solve complex problems in a computationally and energy-efficient way in real time. Within neuromorphic computing, building systems to efficiently store the information is still a challenge. Among all the brain regions, the hippocampus stands out as a short-term memory capable of learning and recalling large amounts of information quickly and efficiently. Herein, a spike-based bio-inspired hippocampus sequential memory model is proposed that makes use of the benefits of analog computing and spiking neural networks (SNNs): noise robustness, improved real-time operation, and energy efficiency. This model is applied to robotic navigation to learn and recall trajectories that lead to a goal position within a known grid environment. The model is implemented on the special-purpose SNNs mixed-signal DYNAP-SE hardware platform. Through extensive experimentation together with an extensive analysis of the model's behavior in the presence of external noise sources, its correct functioning is demonstrated, proving the robustness and consistency of the proposed neuromorphic sequential memory system.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/163536
https://doi.org/10.1002/aisy.202400282
url https://hdl.handle.net/11441/163536
https://doi.org/10.1002/aisy.202400282
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv TED2021-130825B-I00
PID2019-105556GB-C33
PDC2023-145841-C33
FPU20/01994
PID2023-149071NB-C54
https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202400282
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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