AI 中文总结
本研究建立定量框架，利用控制理论定义计算 affordance 景观，揭示大脑网络结构与计算成本的关系，发现人类脑网络及训练后的循环神经网络的景观异质性与功能角色相关。
AI 中文摘要
大脑通过底层网络上的协调活动模式来支持计算，这些网络——从昆虫的微观导航回路到人类的宏观脑区——以结构化方式组织，被认为支撑着其功能。我们寻求一个统一的定量框架，以理解网络结构如何塑造网络可轻松支持的计算。为此，我们将计算表述为活动的目标导向转换，并利用控制理论在给定网络上量化其成本。随后，我们将所有可能转换的成本分布定义为“计算 affordance 景观”（computational affordance landscape），该景观编码了网络结构可轻松支持的计算类型。我们将该框架应用于昆虫维持方向感的回路模型，结果显示，更新方向是成本最低的计算，其预测输入与已知回路一致。在人类大脑中，我们发现 affordance 景观随每个网络的功能角色呈系统性变化：感觉网络呈现更多异质景观（反映其在专门信息处理中的作用），而关联网络呈现更多同质景观（反映其在通用信息处理中的作用）。在经认知任务训练的循环神经网络中，我们发现学习会逐步增加景观异质性，重塑可负担计算的分布。总体而言，我们建立了一个用于研究神经回路中结构与计算之间关系的定量框架，未来应用可扩展至其他生物和物理网络。
英文摘要
The brain supports computations through coordinated patterns of activity on an underlying network. These networks---from microscale navigational circuits in insects to macroscale brain areas in humans---are organized in structured ways that are thought to support their function. We seek a unifying quantitative framework to understand how network structure shapes the computations a network can readily support. To do so, we frame computation as a goal-directed transition of activity and quantify its cost on a given network using control theory. We then define the distribution of costs across all possible transitions as a $\textit{computational affordance landscape}$ that encodes which computations a network structure readily supports. We apply this framework to a circuit model for how insects maintain a sense of direction and show that updating orientation is the least costly computation, with predicted inputs consistent with known circuitry. In the human brain, we find that the affordance landscape varies systematically with the functional role of each network. Sensory networks display more heterogeneous landscapes (reflecting their role in specialized information processing), whereas association networks display more homogeneous landscapes (reflecting their role in generalized information processing). In recurrent neural networks trained on cognitive tasks, we show that learning progressively increases landscape heterogeneity, reshaping the distribution of affordable computations. Generally, we establish a quantitative framework for studying relationships between structure and computation in neural circuits, with future applications extending to other biological and physical networks.
