A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory Model

The brain is capable of solving complex problems simply and efficiently, far surpassing modern computers. In this regard, neuromorphic engineering focuses on mimicking the basic principles that govern the brain in order to develop systems that achieve such computational capabilities. Within this fie...

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Authors: Casanueva Morato, Daniel, Ayuso Martínez, Álvaro, Domínguez Morales, Juan Pedro, Jiménez Fernández, Ángel Francisco, Jiménez Moreno, Gabriel
Format: article
Status:Versión aceptada para publicación
Publication Date:2024
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/168926
Online Access:https://hdl.handle.net/11441/168926
https://doi.org/10.1109/TETC.2024.3387026
Access Level:Open access
Keyword:Hippocampus model
Spiking neural networks
Neuromorphic engineering
CA3
SpiNNaker
id ES_0048b80d0cb2ecfd065bcfb9990f8751
oai_identifier_str oai:idus.us.es:11441/168926
network_acronym_str ES
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repository_id_str
spelling A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory ModelCasanueva Morato, DanielAyuso Martínez, ÁlvaroDomínguez Morales, Juan PedroJiménez Fernández, Ángel FranciscoJiménez Moreno, GabrielHippocampus modelSpiking neural networksNeuromorphic engineeringCA3SpiNNakerThe brain is capable of solving complex problems simply and efficiently, far surpassing modern computers. In this regard, neuromorphic engineering focuses on mimicking the basic principles that govern the brain in order to develop systems that achieve such computational capabilities. Within this field, bio-inspired learning and memory systems are still a challenge to be solved, and this is where the hippocampus is involved. It is the region of the brain that acts as a short-term memory, allowing the learning and storage of information from all the sensory nuclei of the cerebral cortex and its subsequent recall. In this work, we propose a novel bio-inspired hippocampal memory model with the ability to learn memories, recall them from a fragment of itself (cue) and even forget memories when trying to learn others with the same cue. This model has been implemented on SpiNNaker using Spiking Neural Networks, and a set of experiments were performed to demonstrate its correct operation. This work presents the first simulation implemented on a special-purpose hardware platform for Spiking Neural Networks of a fully functional bio-inspired spike-based hippocampus memory model, paving the road for the development of future more complex neuromorphic systems.Institute of Electrical and Electronics EngineersArquitectura y Tecnología de ComputadoresTEP108: Robótica y Tecnología de ComputadoresMinisterio de Ciencia e Innovación (MICIN). España2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/168926https://doi.org/10.1109/TETC.2024.3387026reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE Transactions on Emerging Topics in Computing.PID2019-105556GB-C33https://ieeexplore.ieee.org/document/10502330info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1689262026-06-17T12:51:07Z
dc.title.none.fl_str_mv A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory Model
title A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory Model
spellingShingle A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory Model
Casanueva Morato, Daniel
Hippocampus model
Spiking neural networks
Neuromorphic engineering
CA3
SpiNNaker
title_short A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory Model
title_full A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory Model
title_fullStr A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory Model
title_full_unstemmed A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory Model
title_sort A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory Model
dc.creator.none.fl_str_mv Casanueva Morato, Daniel
Ayuso Martínez, Álvaro
Domínguez Morales, Juan Pedro
Jiménez Fernández, Ángel Francisco
Jiménez Moreno, Gabriel
author Casanueva Morato, Daniel
author_facet Casanueva Morato, Daniel
Ayuso Martínez, Álvaro
Domínguez Morales, Juan Pedro
Jiménez Fernández, Ángel Francisco
Jiménez Moreno, Gabriel
author_role author
author2 Ayuso Martínez, Álvaro
Domínguez Morales, Juan Pedro
Jiménez Fernández, Ángel Francisco
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 e Innovación (MICIN). España
dc.subject.none.fl_str_mv Hippocampus model
Spiking neural networks
Neuromorphic engineering
CA3
SpiNNaker
topic Hippocampus model
Spiking neural networks
Neuromorphic engineering
CA3
SpiNNaker
description The brain is capable of solving complex problems simply and efficiently, far surpassing modern computers. In this regard, neuromorphic engineering focuses on mimicking the basic principles that govern the brain in order to develop systems that achieve such computational capabilities. Within this field, bio-inspired learning and memory systems are still a challenge to be solved, and this is where the hippocampus is involved. It is the region of the brain that acts as a short-term memory, allowing the learning and storage of information from all the sensory nuclei of the cerebral cortex and its subsequent recall. In this work, we propose a novel bio-inspired hippocampal memory model with the ability to learn memories, recall them from a fragment of itself (cue) and even forget memories when trying to learn others with the same cue. This model has been implemented on SpiNNaker using Spiking Neural Networks, and a set of experiments were performed to demonstrate its correct operation. This work presents the first simulation implemented on a special-purpose hardware platform for Spiking Neural Networks of a fully functional bio-inspired spike-based hippocampus memory model, paving the road for the development of future more complex neuromorphic systems.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/168926
https://doi.org/10.1109/TETC.2024.3387026
url https://hdl.handle.net/11441/168926
https://doi.org/10.1109/TETC.2024.3387026
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Emerging Topics in Computing.
PID2019-105556GB-C33
https://ieeexplore.ieee.org/document/10502330
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 Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
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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