dc.creator | Casanueva Morato, Daniel | es |
dc.creator | Ayuso Martínez, Álvaro | es |
dc.creator | Domínguez Morales, Juan Pedro | es |
dc.creator | Jiménez Fernández, Ángel Francisco | es |
dc.creator | Jiménez Moreno, Gabriel | es |
dc.date.accessioned | 2022-11-14T08:22:39Z | |
dc.date.available | 2022-11-14T08:22:39Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Casanueva Morato, D., Ayuso Martínez, Á., Domínguez Morales, J.P., Jiménez Fernández, Á.F. y Jiménez Moreno, G. (2022). Spike-based computational models of bio-inspired memories in the hippocampal CA3 region on SpiNNaker. En IJCNN 2022: International Joint Conference on Neural Networks Padua, Italy: IEEE Computer Society. | |
dc.identifier.isbn | 978-1-7281-8671-9 | es |
dc.identifier.issn | 2161-4407 | es |
dc.identifier.uri | https://hdl.handle.net/11441/139367 | |
dc.description.abstract | The human brain is the most powerful and efficient
machine in existence today, surpassing in many ways the ca pabilities of modern computers. Currently, lines of research in
neuromorphic engineering are trying to develop hardware that
mimics the functioning of the brain to acquire these superior
capabilities. One of the areas still under development is the
design of bio-inspired memories, where the hippocampus plays
an important role. This region of the brain acts as a short-term
memory with the ability to store associations of information from
different sensory streams in the brain and recall them later. This
is possible thanks to the recurrent collateral network architecture
that constitutes CA3, the main sub-region of the hippocampus. In
this work, we developed two spike-based computational models
of fully functional hippocampal bio-inspired memories for the
storage and recall of complex patterns implemented with spiking
neural networks on the SpiNNaker hardware platform. These
models present different levels of biological abstraction, with
the first model having a constant oscillatory activity closer to
the biological model, and the second one having an energy efficient regulated activity, which, although it is still bio-inspired,
opts for a more functional approach. Different experiments were
performed for each of the models, in order to test their learn ing/recalling capabilities. A comprehensive comparison between
the functionality and the biological plausibility of the presented
models was carried out, showing their strengths and weaknesses.
The two models, which are publicly available for researchers,
could pave the way for future spike-based implementations and
applications. | es |
dc.description.sponsorship | Agencia Estatal de Investigación PID2019-105556GB-C33/AEI/10.13039/501100011033 (MINDROB) | es |
dc.format | application/pdf | es |
dc.format.extent | 9 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IJCNN 2022: International Joint Conference on Neural Networks (2022). | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Hippocampus model | es |
dc.subject | CA3 | es |
dc.subject | Neuromorphic engineering | es |
dc.subject | Spiking Neural Networks | es |
dc.subject | SpiNNaker | es |
dc.subject | Spike-based memory | es |
dc.title | Spike-based computational models of bio-inspired memories in the hippocampal CA3 region on SpiNNaker | es |
dc.type | info:eu-repo/semantics/conferenceObject | 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 | PID2019-105556GB-C33/AEI/10.13039/501100011033 (MINDROB) | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9892606 | es |
dc.identifier.doi | 10.1109/IJCNN55064.2022.9892606 | es |
dc.contributor.group | Universidad de Sevilla. TEP108 : Robotica y Tecnología de Computadores | es |
dc.eventtitle | IJCNN 2022: International Joint Conference on Neural Networks | es |
dc.eventinstitution | Padua, Italy | es |
dc.relation.publicationplace | New York, USA | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |