Search for: All records

Deep neural networks (DNNs) have achieved remarkable success in various cognitive tasks through training on extensive labeled datasets. However, the heavy reliance on these datasets poses challenges for DNNs in scenarios with energy constraints in particular scenarios, such as on the moon. On the contrary, animals exhibit a self-learning capability by interacting with their surroundings and memorizing concurrent events without annotated data—a process known as associative learning. A classic example of associative learning is when a rat memorizes desired and undesired stimuli while exploring a T-maze. The successful implementation of associative learning aims to replicate the self-learning mechanisms observed in animals, addressing challenges in data-constrained environments. While current implementations of associative learning are predominantly small scale and offline, this work pioneers associative learning in a robot equipped with a neuromorphic chip, specifically for online learning in a T-maze. The system successfully replicates classic associative learning observed in rodents, using neuromorphic robots as substitutes for rodents. The neuromorphic robot autonomously learns the cause-and-effect relationship between audio and visual stimuli.