Margalit Eshed, Lee Hyodong, Finzi Dawn, DiCarlo James J, Grill-Spector Kalanit, Yamins Daniel L K
Neurosciences Graduate Program, Stanford University, Stanford, CA 94305.
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139.
bioRxiv. 2023 May 18:2023.05.18.541361. doi: 10.1101/2023.05.18.541361.
A key feature of many cortical systems is functional organization: the arrangement of neurons with specific functional properties in characteristic spatial patterns across the cortical surface. However, the principles underlying the emergence and utility of functional organization are poorly understood. Here we develop the Topographic Deep Artificial Neural Network (TDANN), the first unified model to accurately predict the functional organization of multiple cortical areas in the primate visual system. We analyze the key factors responsible for the TDANN's success and find that it strikes a balance between two specific objectives: achieving a task-general sensory representation that is self-supervised, and maximizing the smoothness of responses across the cortical sheet according to a metric that scales relative to cortical surface area. In turn, the representations learned by the TDANN are lower dimensional and more brain-like than those in models that lack a spatial smoothness constraint. Finally, we provide evidence that the TDANN's functional organization balances performance with inter-area connection length, and use the resulting models for a proof-of-principle optimization of cortical prosthetic design. Our results thus offer a unified principle for understanding functional organization and a novel view of the functional role of the visual system in particular.
具有特定功能特性的神经元在整个皮质表面以特征性的空间模式排列。然而，功能组织的出现和效用背后的原理却知之甚少。在此，我们开发了地形深度人工神经网络（TDANN），这是首个能准确预测灵长类视觉系统中多个皮质区域功能组织的统一模型。我们分析了TDANN成功的关键因素，发现它在两个特定目标之间取得了平衡：实现一个自我监督的通用任务感觉表征，并根据一个相对于皮质表面积缩放的度量标准，使跨皮质层的反应平滑度最大化。反过来，与缺乏空间平滑度约束的模型相比，TDANN学习到的表征维度更低且更类似大脑。最后，我们提供证据表明，TDANN的功能组织在性能与区域间连接长度之间取得了平衡，并将所得模型用于皮质假体设计的原理验证优化。因此，我们的结果为理解功能组织提供了一个统一原则，特别是为视觉系统的功能作用提供了一个全新视角。
