在宽度、深度与时间上扩展神经网络

摘要

空间和时间资源约束对生物与人工智能系统均至关重要。本文在递归卷积神经网络中定义了宽度、深度与时间这三个可微分的代价项，该网络被视为无限格点结构中的一个有限子集。我们通过反向传播算法，将这些代价与任务误差联合优化。通过施加不同强度的宽度、深度与时间约束，不同计算图在训练过程中得以有机涌现。研究发现，为实现特定准确率水平，这三种资源可以相互权衡。网络在三个维度上随任务复杂度增长，并且在输入被遮挡时会自发增加递归步数。令人惊讶的是，模型使用的处理时间与人类在物体识别任务中的反应时间存在相关性。本框架为资源约束如何塑造神经架构提供了规范性解释，既关联神经科学中关于大脑设计的核心问题，又有助于阐明自然界中神经解决方案的多样性。

English

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.