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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400282 |
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| author | Daniel Casanueva‐Morato Alvaro Ayuso‐Martinez Giacomo Indiveri Juan P. Dominguez‐Morales Gabriel Jimenez‐Moreno |
| author_facet | Daniel Casanueva‐Morato Alvaro Ayuso‐Martinez Giacomo Indiveri Juan P. Dominguez‐Morales Gabriel Jimenez‐Moreno |
| author_sort | Daniel Casanueva‐Morato |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-2bd97f9f347541dbbc87b210dee6b322 |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-2bd97f9f347541dbbc87b210dee6b3222025-01-21T07:26:27ZengWileyAdvanced Intelligent Systems2640-45672025-01-0171n/an/a10.1002/aisy.202400282Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis OverviewDaniel Casanueva‐Morato0Alvaro Ayuso‐Martinez1Giacomo Indiveri2Juan P. Dominguez‐Morales3Gabriel Jimenez‐Moreno4Robotics and Technology of Computers Lab. Universidad de Sevilla Sevilla SpainRobotics and Technology of Computers Lab. Universidad de Sevilla Sevilla SpainInstitute of Neuroinformatics University of Zurich and ETH Zurich Zurich 8057 SwitzerlandRobotics and Technology of Computers Lab. Universidad de Sevilla Sevilla SpainRobotics and Technology of Computers Lab. Universidad de Sevilla Sevilla SpainThe 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.https://doi.org/10.1002/aisy.202400282analog sequential memoryDYNAP‐SEhippocampus modelneuromorphic engineeringrobustness analysisspiking neural networks |
| spellingShingle | Daniel Casanueva‐Morato Alvaro Ayuso‐Martinez Giacomo Indiveri Juan P. Dominguez‐Morales Gabriel Jimenez‐Moreno Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview Advanced Intelligent Systems analog sequential memory DYNAP‐SE hippocampus model neuromorphic engineering robustness analysis spiking neural networks |
| title | 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_short | 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 |
| topic | analog sequential memory DYNAP‐SE hippocampus model neuromorphic engineering robustness analysis spiking neural networks |
| url | https://doi.org/10.1002/aisy.202400282 |
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