# 神经网络在宽度、深度与时间中的生长

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-24 08:00
- AIHOT 分数：64
- AIHOT 链接：https://aihot.virxact.com/items/cmpppl0wm027rslvyyk72q4ov
- 原文链接：https://arxiv.org/abs/2605.25174

## AI 摘要

该研究在循环卷积神经网络中，将宽度、深度和时间定义为可微分成本项，并与任务误差一同通过反向传播进行联合优化。通过施加不同压力，多样化的计算图在训练中自然涌现。研究发现，这三种资源可以相互权衡以达到特定准确率。网络规模随任务复杂度在三个维度上增长，并在输入被遮挡时自发增加循环步数。模型使用的时间与人类在物体识别任务中的反应时间存在相关性。

## 正文

Spatial and temporal resource constraints are critical for both biological and artificial intelligent systems. Here we define differentiable cost terms for breadth, depth, and time within a recurrent convolutional neural network conceived as a finite subset of an infinite lattice. We optimize these costs jointly with task errors via backpropagation. We set different pressures on breadth, depth, and time, which leads to diverse computational graphs emerging organically through training. We find that all three resources can be traded off against each other to achieve a given level of accuracy. Networks grow in all three dimensions with task complexity and spontaneously take more recurrent steps when inputs are occluded. Surprisingly, time used by the model correlates with human reaction times in an object recognition task. Our framework provides a normative account of how resource constraints shape neural architectures, connecting to questions about brain design in neuroscience, and may help illuminate the diversity of neural solutions found in nature.