A central idea in understanding brains and building artificial intelligence is that structure determines function. Yet, how the brain's complex structure arises from a limited set of genetic instructions remains a key question. The ultra high-dimensional detail of neural connections vastly exceeds the information storage capacity of genes, suggesting a compact, low-dimensional blueprint must guide brain development. Our motivation is to uncover this blueprint. We introduce a generative model, to learn this underlying representation from detailed connectivity maps of mouse cortical microcircuits. Our model successfully captures the essential structural information of these circuits in a compressed latent space. We found that specific, interpretable directions within this space directly relate to understandable network properties. Building on this, we demonstrate a novel method to controllably generate new, synthetic microcircuits with desired structural features by navigating this latent space. This work offers a new way to investigate the design principles of neural circuits and explore how structure gives rise to function, potentially informing the development of more advanced artificial neural networks.

翻译：理解大脑与构建人工智能的一个核心观点是结构决定功能。然而，大脑的复杂结构如何从有限的遗传指令中产生，仍然是一个关键问题。神经连接的超高维细节远超基因的信息存储容量，这表明必须有一个紧凑的低维蓝图来指导大脑发育。我们的动机正是要揭示这一蓝图。我们引入了一种生成模型，旨在从小鼠皮层微环路的详细连接图谱中学习这种底层表征。我们的模型成功地将这些环路的基本结构信息压缩并捕获在一个潜在空间中。我们发现，该空间内特定、可解释的方向直接与可理解的网络特性相关。在此基础上，我们展示了一种新颖的方法，通过在该潜在空间中导航，可控地生成具有所需结构特征的新合成微环路。这项工作为研究神经环路的设计原理以及探索结构如何产生功能提供了一种新途径，并可能为开发更先进的人工神经网络提供启示。