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 physics-informed Hamiltonian neural networks learning H\'enon-Heiles stellar orbits and the swing of a video recorded pendulum clock. Such \textit{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化的神经元构成。本文通过构建由自身学习激活函数的神经元组成的神经网络，这些神经元能快速实现多样化，并在图像分类和非线性回归任务中展现超越同质化网络的性能。子网络实例化这些神经元，并通过元学习获得特别高效的非线性响应集合。研究案例涵盖：用于数字分类和范德波尔振荡器预测的传统神经网络，以及学习Hénon-Heiles恒星轨道运动和视频记录摆钟摆动的物理信息哈密顿神经网络。这种"习得多样性"为动力系统选择多样性而非同一性提供了范例，并阐释了多样性在自然系统与人工系统中的作用。