用进化搜索找到与大脑视觉皮层匹配的网络，发现其具有清晰层次结构。
Cognitive Neural Architecture Search Reveals Hierarchical Entailment
- 通过进化算法优化网络结构以匹配大脑反应
- 随机权重模型脑对齐得分超过预训练分类模型
- 为计算认知神经科学提供自动化建模新范式
最新研究认为大脑视觉通路比以往认为的更浅，挑战了传统层级结构假设。本文通过进化神经架构搜索优化卷积网络结构以实现与大脑的对齐，发现所获模型具备清晰的表征层次。即使在随机权重下，这些模型的脑对齐分数也超过了预训练分类模型——通过回归和表征相似性分析验证。此外，经过传统监督训练后，专为晚期腹侧区域对齐优化的架构成为具有竞争力的分类模型。结果表明，层级结构是灵长类视觉处理的根本机制。本研究展示了神经架构搜索在计算认知神经科学中的潜力，可减少对人工设计卷积网络的依赖。
原文摘要 · Abstract (English)
Recent research has suggested that the brain is more shallow than previously thought, challenging the traditionally assumed hierarchical structure of the ventral visual pathway. Here, we demonstrate that optimizing convolutional network architectures for brain-alignment via evolutionary neural architecture search results in models with clear representational hierarchies. Despite having random weights, the identified models achieve brain-alignment scores surpassing even those of pretrained classification models - as measured by both regression and representational similarity analysis. Furthermore, through traditional supervised training, architectures optimized for alignment with late ventral regions become competitive classification models. These findings suggest that hierarchical structure is a fundamental mechanism of primate visual processing. Finally, this work demonstrates the potential of neural architecture search as a framework for computational cognitive neuroscience research that could reduce the field's reliance on manually designed convolutional networks.
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