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<p>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.</p>

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A Bio-realistic Synthetic Hippocampus for Robotic Cognition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月21日

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  • Jordi Vallverdu,
  • Xenia Feinstein,
  • Paul Robertson,
  • Ivan Kipelkin,
  • Max Talanov

摘要

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.