用生成模型解码小鼠皮层微电路的结构蓝图，可控制生成新神经网络。
Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesis
- 构建生成模型从详细连接图中学习压缩的潜在空间表示。
- 潜在空间中特定方向对应可解释的网络属性，如连接密度或层级结构。
- 通过导航潜在空间，可可控生成具有目标结构特征的合成微电路。
理解大脑与构建人工智能的核心理念是：结构决定功能。然而，大脑复杂结构如何由有限的基因指令形成仍是关键问题。神经连接的超高维细节远超基因信息存储能力，暗示存在一个紧凑的低维蓝图引导发育。本文旨在揭示这一蓝图。我们提出一种生成模型，从小鼠皮层微电路的详细连接图中学习其底层表示。模型成功在压缩的潜在空间中捕捉这些电路的关键结构信息。我们发现，潜在空间中的特定可解释方向直接关联可理解的网络特性。基于此，我们展示了一种新方法，可通过导航潜在空间可控生成具有期望结构特征的新型合成微电路。该工作为研究神经回路设计原则、探索结构如何产生功能提供了新路径，可能推动更先进人工神经网络的发展。
原文摘要 · Abstract (English)
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.
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