Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of artificial neural networks. Here we construct neural networks from neurons that learn their own activation functions, quickly diversify, and subsequently outperform their homogeneous counterparts on image classification and nonlinear regression tasks. Sub-networks instantiate the neurons, which meta-learn especially efficient sets of nonlinear responses. Examples include conventional neural networks classifying digits and forecasting a van der Pol oscillator and a physics-informed Hamiltonian neural network learning H\'enon-Heiles orbits. Such learned diversity provides examples of dynamical systems selecting diversity over uniformity and elucidates the role of diversity in natural and artificial systems.

翻译：多样性在自然界中具有优势，然而人工神经网络的各层通常由同质神经元构成。本研究构建了由能够自主学习激活函数并快速实现多样化的神经元组成的神经网络。在图像分类与非线性回归任务中，这些网络的表现超越了传统的同质化对应模型。每个神经元由子网络实例化实现，通过元学习获得高效的非线性响应组合。典型案例包括：用于数字分类的传统神经网络、范德波尔振荡器预测模型，以及学习埃农-海尔斯轨道的物理信息哈密顿神经网络。这种习得的多样性为动力系统在选择多样性与一致性时提供了例证，并阐明了多样性在自然与人工系统中的作用。