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A Brain-Inspired Model of Hippocampal Spatial Cognition Based on a Memory-Replay Mechanism
Runyu Xu1,2, Xiaogang Ruan1,2, Jing Huang1,2
1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
Brain Sciences
|September 23, 2022
Summary
This study introduces a novel Memory-Replay Mechanism inspired by the hippocampus to improve spatial cognition and path planning in artificial agents. The brain-inspired model enhances environmental exploration and navigation performance, especially in complex environments.
Area of Science:
- Neuroscience
- Cognitive Science
- Artificial Intelligence
Background:
- The hippocampus is crucial for memory and spatial cognition.
- Current spatial computation models struggle with decaying reward signals in large environments.
- This limitation hinders performance in complex navigation tasks.
Purpose of the Study:
- To develop a brain-inspired mechanism to overcome reward signal attenuation in spatial navigation models.
- To enhance spatial cognition and path planning capabilities in artificial agents.
- To improve the performance of models in complex and large-scale environments.
Main Methods:
- A novel Memory-Replay Mechanism inspired by hippocampal place cell reactivation was developed.
- Path memory was classified by reward information, and overlapping place cells were identified.
- Memory segmentation and reconstruction formed a 'virtual path' for replay, associated with reward information.
Main Results:
- The Memory-Replay Mechanism demonstrated a higher environmental exploration rate and more stable signal transmission compared to reinforcement learning models.
- The model achieved 14.12% higher average reward in stable conditions than reinforcement learning with random-experience replay.
- The model exhibited robust performance in complex maze environments and consistent behavior at bifurcations, aligning with neurophysiological studies.
Conclusions:
- The proposed Memory-Replay Mechanism effectively addresses the challenge of reward signal attenuation in spatial navigation.
- This brain-inspired approach significantly enhances navigation performance and environmental exploration in artificial agents.
- The model's consistency with neurophysiological findings suggests its potential for advanced cognitive robotics and AI research.
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