No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks

作者：Fenglei Fan, Meng Wang, Hangcheng Dong, Jianwei Ma, Tieyong Zeng · 发表于：IEEE Transactions on Pattern Analysis and Machine Intelligence · 年份：2026 · DOI：10.1109/tpami.2026.3704202 · 被引用次数：1 · 研究领域：Neural Networks and Applications

In the past decade, many successful networks are on novel architectures, which almost exclusively use the same type of neurons. Recently, more and more deep learning studies have been inspired by the idea of NeuroAI and the neuronal diversity observed in human brains, leading to the proposal of novel artificial neuron designs. Designing well-performing neurons represents a new dimension relative to designing well-performing neural architectures. Biologically, the brain does not rely on a single type of neuron that universally functions in all aspects. Instead, in our brain, neurons are often task-based. In this study, we address the following question: since the human brain is a task-based neuron user, can the artificial network design go from the task-based architecture design to the task-based neuron design? Since methodologically there are no one-size-fits-all neurons, given the same structure, task-based neurons can enhance the feature representation ability relative to the existing universal neurons due to the intrinsic inductive bias for the task. Specifically, we propose a two-step framework for prototyping task-based neurons. First, symbolic regression is used to identify optimal formulas that fit input data by utilizing base functions such as polynomials. We introduce VSR that stacks all variables in a vector and regularizes each input variable to perform the same computation, which can increase the regression speed, facilitate efficacy in high dimensions, and enable...