The spatial and functional organization of the primate visual cortex is a fundamental problem in neuroscience. While recent computational frameworks like the Topographic Deep Artificial Neural Network (TDANN) have successfully modeled spatial organization in the ventral stream, the computational origins of the dorsal stream's distinct topographies, such as direction-selective maps in the middle temporal (MT) area, remain largely unresolved. In this work, we present a spatiotemporal TDANN to investigate whether MT topography is governed by the same universal principles. By training a 3D ResNet on naturalistic videos via a Momentum Contrast (MoCo) self-supervised paradigm alongside a biologically inspired spatial loss, we demonstrate the spontaneous emergence of brain-like direction maps and topological pinwheel structures. Crucially, we reveal that MT tuning properties, characterized by strong direction selectivity paired with a residual axial component, arise from a strict optimization trade-off between task-driven discriminative pressure and spatial regularization. The model's representations quantitatively match in vivo macaque MT physiological baselines, including direction selectivity index, circular variance, and pinwheel density. These findings unify the computational origins of the ventral and dorsal streams, establishing a general mechanism for cortical self-organization.

翻译：灵长类视觉皮层的空间与功能组织是神经科学的一个基本问题。尽管近年来诸如拓扑深度人工神经网络（TDANN）等计算框架已成功模拟了腹侧通路中的空间组织，但背侧通路独特拓扑结构（如中颞区（MT）的方向选择性图谱）的计算起源在很大程度上仍未解决。在本研究中，我们提出了一种时空TDANN，以探究MT拓扑结构是否受相同的普遍原理支配。通过在自然视频上使用动量对比（MoCo）自监督学习范式，并结合受生物启发的空间损失函数训练3D ResNet，我们验证了类脑方向图与拓扑辐轮结构的自发涌现。关键在于，我们发现MT调谐特性——以强方向选择性伴随残余轴向分量为特征——源于任务驱动的判别性压力与空间正则化之间的严格优化权衡。该模型的表征在数量上匹配了体内猴MT的生理基线，包括方向选择性指数、循环方差和辐轮密度。这些发现统一了腹侧和背侧通路的计算起源，为皮层自组织建立了一个通用机制。