As our understanding of the mechanisms of brain function is enhanced, the value of insights gained from neuroscience to the development of AI algorithms deserves further consideration. Here, we draw parallels with an existing tree-based ANN architecture and a recent neuroscience study[27] arguing that the error-based organization of neurons in the cerebellum that share a preference for a personalized view of the entire error space, may account for several desirable features of behavior and learning. We then analyze the learning behavior and characteristics of the model under varying scenarios to gauge the potential benefits of a similar mechanism in ANN. Our empirical results suggest that having separate populations of neurons with personalized error views can enable efficient learning under class imbalance and limited data, and reduce the susceptibility to unintended shortcut strategies, leading to improved generalization. This work highlights the potential of translating the learning machinery of the brain into the design of a new generation of ANNs and provides further credence to the argument that biologically inspired AI may hold the key to overcoming the shortcomings of ANNs.

翻译：随着我们对脑功能机制理解的加深，神经科学为人工智能算法发展所提供的洞见价值值得进一步探讨。本文类比现有基于树结构的人工神经网络架构与最新神经科学研究[27]——该研究主张小脑中对整个误差空间具有个性化偏好的神经元按误差组织，可能解释了行为与学习的若干理想特征。随后我们分析了该模型在不同场景下的学习行为与特性，以评估人工神经网络中类似机制的潜在效益。实证结果表明：拥有个性化误差视图的独立神经元群体，能在类别不平衡与数据有限条件下实现高效学习，并降低对非预期捷径策略的敏感性，从而提升泛化能力。本研究凸显了将大脑学习机制转化为新一代人工神经网络设计的潜力，并为"生物启发式人工智能可能是克服人工神经网络缺陷的关键"这一论点提供了进一步佐证。