受大脑皮层启发，构建可解释的模块化感知AI架构
A Cortically Inspired Architecture for Modular Perceptual AI
- 将感知任务拆分为相互协作的专用模块，模拟大脑皮层结构
- 通过层级预测反馈与共享隐空间，实现更稳定可检视的表征
- 适合追求可解释性与类人推理的AI系统研发者
本文融合神经科学与人工智能，提出一种受大脑皮层启发的模块化感知AI蓝图。当前如GPT-4V等单体模型虽表现优异，却难以显式支持可解释性、组合泛化与自适应鲁棒性——人类认知的核心特征。基于皮层模块化、预测加工及跨模态整合的神经科学模型，我们主张将感知分解为专业化、交互式的模块。该架构通过层级预测反馈回路和共享隐空间，使内部推理过程显式化，支持结构化的人类式推理。概念验证研究证实，模块化分解能生成更稳定且可检视的表征。通过植根于生物验证原则的AI设计，我们朝着不仅性能优异，且具备透明与人类对齐推理能力的系统迈进。
原文摘要 · Abstract (English)
This paper bridges neuroscience and artificial intelligence to propose a cortically inspired blueprint for modular perceptual AI. While current monolithic models such as GPT-4V achieve impressive performance, they often struggle to explicitly support interpretability, compositional generalization, and adaptive robustness - hallmarks of human cognition. Drawing on neuroscientific models of cortical modularity, predictive processing, and cross-modal integration, we advocate decomposing perception into specialized, interacting modules. This architecture supports structured, human-inspired reasoning by making internal inference processes explicit through hierarchical predictive feedback loops and shared latent spaces. Our proof-of-concept study provides empirical evidence that modular decomposition yields more stable and inspectable representations. By grounding AI design in biologically validated principles, we move toward systems that not only perform well, but also support more transparent and human-aligned inference.
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