用可微渲染3D建模，揭示神经元对真实场景属性的响应机制
Beyond Pixels: A Differentiable Pipeline for Probing Neuronal Selectivity in 3D
- 通过径向基函数参数化3D网格变形，直接优化生成刺激
- 在猴子V4区模型中成功分离出对姿态、光照等3D因素的选择性
- 适合研究视觉神经机制或需物理可解释刺激的神经科学工作者
视觉感知依赖于对三维场景属性（如形状、姿态、光照）的推断。为理解视觉神经元如何支持鲁棒感知，必须刻画其对这些物理可解释因素的敏感性。然而，现有方法多基于2D像素，难以分离对真实物理属性的选择性。为此，我们提出一种可微渲染流程，通过优化可变形网格直接生成三维最大化神经反应的刺激（MEIs）。该方法使用径向基函数参数化网格形变，学习偏移与缩放以最大化神经响应，同时保持几何规则性。应用于猴子视皮层区域V4的模型，该方法成功探测到神经元对姿态、光照等可解释3D因素的选择性。此方法连接逆图形学与系统神经科学，提供了一种超越传统像素级方法、基于物理基础的3D刺激探针方案。
原文摘要 · Abstract (English)
Visual perception relies on inference of 3D scene properties such as shape, pose, and lighting. To understand how visual sensory neurons enable robust perception, it is crucial to characterize their selectivity to such physically interpretable factors. However, current approaches mainly operate on 2D pixels, making it difficult to isolate selectivity for physical scene properties. To address this limitation, we introduce a differentiable rendering pipeline that optimizes deformable meshes to obtain MEIs directly in 3D. The method parameterizes mesh deformations with radial basis functions and learns offsets and scales that maximize neuronal responses while enforcing geometric regularity. Applied to models of monkey area V4, our approach enables probing neuronal selectivity to interpretable 3D factors such as pose and lighting. This approach bridges inverse graphics with systems neuroscience, offering a way to probe neural selectivity with physically grounded, 3D stimuli beyond conventional pixel-based methods.
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