Our comprehension of biological neuronal networks has profoundly influenced the evolution of artificial neural networks (ANNs). However, the neurons employed in ANNs exhibit remarkable deviations from their biological analogs, mainly due to the absence of complex dendritic trees encompassing local nonlinearity. Despite such disparities, previous investigations have demonstrated that point neurons can functionally substitute dendritic neurons in executing computational tasks. In this study, we scrutinized the importance of nonlinear dendrites within neural networks. By employing machine-learning methodologies, we assessed the impact of dendritic structure nonlinearity on neural network performance. Our findings reveal that integrating dendritic structures can substantially enhance model capacity and performance while keeping signal communication costs effectively restrained. This investigation offers pivotal insights that hold considerable implications for the development of future neural network accelerators.

翻译：我们对生物神经网络的认知深刻影响了人工神经网络（ANNs）的演进。然而，ANNs中使用的神经元与其生物对应物存在显著差异，主要源于缺乏包含局部非线性的复杂树突树结构。尽管存在这些差异，以往研究表明点神经元在计算任务中可功能性替代树突神经元。在本研究中，我们深入探究了神经网络中非线性树突的重要性。通过采用机器学习方法，评估了树突结构非线性对神经网络性能的影响。研究发现表明，整合树突结构能在有效抑制信号通信成本的同时，显著增强模型容量与性能。这项研究为未来神经网络加速器的开发提供了具有重要启示意义的洞见。