Structural features of the fly olfactory circuit mitigate the stability-plasticity dilemma in continual learning

These authors contribute equally to this work. Abstract Artificial neural networks face the stability-plasticity dilemma in continual learning, while the brain can maintain memories and remain adaptable. However, the biological strategies for continual learning and their potential to inspire learning algorithms in neural networks are poorly understood. This study presents a minimal model of the fly olfactory circuit to investigate the biological strategies that support continual odor learning. We introduce the fly olfactory circuit as a plug-and-play component, termed the Fly Model, which can integrate with modern machine learning methods to address this dilemma. Our findings demonstrate that the Fly Model enhances both memory stability and learning plasticity, overcoming the limitations of current continual learning strategies. We validated its effectiveness across various challenging continual learning scenarios using commonly used datasets. The fly olfactory system serves as an elegant biological circuit for lifelong learning, offering a module that enhances continual learning with minimal additional computational cost for machine learning. When learning new tasks and updating parameters, these models inevitably overwrite previously learned patterns, resulting in "catastrophic forgetting" [1-3]. This critical flaw has become the Achilles' heel of neural network models, preventing them from realizing their full potential. Conversely, especially under long non-stationary data streams, the parameters of network models may become less effective at updating, resulting in a gradual decline in their ability to adapt to new information. This issue, known as plasticity loss, has garnered increasing attention in recent years [4-5].