果蝇嗅觉回路的结构特征缓解了持续学习中的稳定性-可塑性困境
Structural features of the fly olfactory circuit mitigate the stability-plasticity dilemma in continual learning
AI总结：
针对持续学习的稳定性-可塑性困境，提出果蝇嗅觉回路模型Fly Model作为即插即用模块，增强记忆稳定性与学习可塑性，以极低计算成本提升机器学习持续学习能力。
摘要：
人工神经网络在持续学习中面临稳定性-可塑性困境，而大脑却能维持记忆并保持适应性。然而，目前对持续学习的生物策略及其启发神经网络学习算法的潜力知之甚少。本研究提出了一个果蝇嗅觉回路的最小模型，以探究支持持续气味学习的生物策略。我们将果蝇嗅觉回路作为一个即插即用的组件引入，称为Fly Model，它可以与现代机器学习方法集成以解决这一困境。我们的研究结果表明，Fly Model增强了记忆稳定性和学习可塑性，克服了当前持续学习策略的局限性。我们使用常用数据集，在各种具有挑战性的持续学习场景中验证了其有效性。果蝇嗅觉系统作为终身学习的优雅生物回路，提供了一个以最小额外计算成本增强机器学习持续学习的模块。
英文摘要：
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.
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