A Bio-realistic Synthetic Hippocampus for Robotic Cognition
Current robotic systems struggle with adaptive generalisation beyond curated training domains. Inspired by hippocampal dynamics in biological cognition, we introduce a synthetic memory architecture that segregates online sensorimotor interaction from offline consolidation and generative replay. Impl...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:321813 |
| Acceso en línea: | https://ddd.uab.cat/record/321813 https://dx.doi.org/urn:doi:10.1007/s12668-025-02229-2 |
| Access Level: | acceso abierto |
| Palabra clave: | Sleep Hippocampus Robotic Spiking neural networks Memristive device |
| Sumario: | Current robotic systems struggle with adaptive generalisation beyond curated training domains. Inspired by hippocampal dynamics in biological cognition, we introduce a synthetic memory architecture that segregates online sensorimotor interaction from offline consolidation and generative replay. Implemented via spiking neural networks and neuromorphic substrates, our framework enables bidirectional memory traversal, goal-prioritised plasticity updates, and energy-efficient policy synthesis. This dual-state system bridges real-time control with autonomous learning, advancing a biologically grounded pathway toward resilient, context-adaptive robotic intelligence. |
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