Neuronal morphology is essential for studying brain functioning and understanding neurodegenerative disorders. As acquiring real-world morphology data is expensive, computational approaches for morphology generation have been studied. Traditional methods heavily rely on expert-set rules and parameter tuning, making it difficult to generalize across different types of morphologies. Recently, MorphVAE was introduced as the sole learning-based method, but its generated morphologies lack plausibility, i.e., they do not appear realistic enough and most of the generated samples are topologically invalid. To fill this gap, this paper proposes MorphGrower, which mimicks the neuron natural growth mechanism for generation. Specifically, MorphGrower generates morphologies layer by layer, with each subsequent layer conditioned on the previously generated structure. During each layer generation, MorphGrower utilizes a pair of sibling branches as the basic generation block and generates branch pairs synchronously. This approach ensures topological validity and allows for fine-grained generation, thereby enhancing the realism of the final generated morphologies. Results on four real-world datasets demonstrate that MorphGrower outperforms MorphVAE by a notable margin. Importantly, the electrophysiological response simulation demonstrates the plausibility of our generated samples from a neuroscience perspective. Our code is available at https://github.com/Thinklab-SJTU/MorphGrower.

翻译：神经元形态对于研究大脑功能及理解神经退行性疾病至关重要。由于获取真实世界的形态数据成本高昂，研究者开始探索用于形态生成的计算方法。传统方法严重依赖专家设定的规则和参数调整，难以在不同类型的形态间泛化。近期，MorphVAE作为唯一基于学习的方法被提出，但其生成的形态缺乏合理性，即看起来不够真实，且大多数生成样本在拓扑结构上无效。为填补这一空白，本文提出MorphGrower，该方法模仿神经元自然生长机制进行生成。具体而言，MorphGrower逐层生成形态，每一后续层的生成均以前一层已生成的结构为条件。在每一层生成过程中，MorphGrower以一对同级分支作为基本生成单元，并同步生成分支对。该方法确保了拓扑结构的有效性，并支持细粒度生成，从而提升了最终生成形态的真实感。在四个真实数据集上的实验结果表明，MorphGrower显著优于MorphVAE。重要的是，电生理响应模拟从神经科学角度证明了我们生成样本的合理性。我们的代码公开于https://github.com/Thinklab-SJTU/MorphGrower。