Mimicking Associative Learning of Rats via a Neuromorphic Robot in Open Field Maze Using Spatial Cell Models

Data-driven Artificial Intelligence (AI) approaches have exhibited remarkable prowess across various cognitive tasks using extensive training data. However, the reliance on large datasets and neural networks presents challenges such as high-power consumption and limited adaptability, particularly in...

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Veröffentlicht in:2024 International Conference on Neuromorphic Systems (ICONS) S. 299 - 306
Hauptverfasser: Liu, Tianze, Bakr Siddique, Md Abu, An, Hongyu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 30.07.2024
Schlagworte:
Associative Learning
Biological system modeling
Cells (biology)
Cognition
Grid Cells
Navigation
Neuromorphic Robotics
Neuromorphics
Place Cells
Real-time systems
Robots
Rodents
Testing
Training data
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Zusammenfassung:Data-driven Artificial Intelligence (AI) approaches have exhibited remarkable prowess across various cognitive tasks using extensive training data. However, the reliance on large datasets and neural networks presents challenges such as high-power consumption and limited adaptability, particularly in SWaP-constrained applications like planetary exploration. To address these issues, we propose enhancing the autonomous capabilities of intelligent robots by emulating the associative learning observed in animals. Associative learning enables animals to adapt to their environment by memorizing concurrent events. By replicating this mechanism, neuromorphic robots can navigate dynamic environments autonomously, learning from interactions to optimize performance. This paper explores the emulation of associative learning in rodents using neuromorphic robots within open-field maze environments, leveraging insights from spatial cells such as place and grid cells. By integrating these models, we aim to enable online associative learning for spatial tasks in real-time scenarios, bridging the gap between biological spatial cognition and robotics for advancements in autonomous systems.
DOI:10.1109/ICONS62911.2024.00052