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 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.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview ; day:30 ; month:09 ; year:2024 ; extent:16
Advanced intelligent systems ; (30.09.2024) (gesamt 16)
- Creator
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Casanueva‐Morato, Daniel
Ayuso‐Martinez, Alvaro
Indiveri, Giacomo
Dominguez‐Morales, Juan P.
Jimenez‐Moreno, Gabriel
- DOI
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10.1002/aisy.202400282
- URN
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urn:nbn:de:101:1-2410011412560.076563368448
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:27 AM CEST
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Casanueva‐Morato, Daniel
- Ayuso‐Martinez, Alvaro
- Indiveri, Giacomo
- Dominguez‐Morales, Juan P.
- Jimenez‐Moreno, Gabriel
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