While biological neural networks develop from compact genomes using relatively simple rules, modern artificial neural architecture search methods mostly involve explicit and routine manual work. In this paper, we introduce MorphoNAS (Morphogenetic Neural Architecture Search), a system able to deterministically grow neural networks through morphogenetic self-organization inspired by the Free Energy Principle, reaction-diffusion systems, and gene regulatory networks. In MorphoNAS, simple genomes encode just morphogens dynamics and threshold-based rules of cellular development. Nevertheless, this leads to self-organization of a single progenitor cell into complex neural networks, while the entire process is built on local chemical interactions. Our evolutionary experiments focused on two different domains: structural targeting, in which MorphoNAS system was able to find fully successful genomes able to generate predefined random graph configurations (8-31 nodes); and functional performance on the CartPole control task achieving low complexity 6-7 neuron solutions when target network size minimization evolutionary pressure was applied. The evolutionary process successfully balanced between quality of of the final solutions and neural architecture search effectiveness. Overall, our findings suggest that the proposed MorphoNAS method is able to grow complex specific neural architectures, using simple developmental rules, which suggests a feasible biological route to adaptive and efficient neural architecture search.

翻译：虽然生物神经网络通过相对简单的规则从紧凑的基因组发育而成，但现代人工神经架构搜索方法大多涉及明确且常规的手动工作。本文提出了MorphoNAS（形态发生神经架构搜索），一个能够通过受自由能原理、反应-扩散系统和基因调控网络启发的形态发生自组织过程，确定性生长出神经网络的系统。在MorphoNAS中，简单基因组仅编码形态发生素动力学和基于阈值的细胞发育规则。尽管如此，这导致单个祖细胞自组织成复杂神经网络，而整个过程建立在局部化学相互作用之上。我们的进化实验聚焦于两个不同领域：结构靶向，其中MorphoNAS系统能够找到完全成功的基因组，以生成预定义的随机图配置（8-31个节点）；以及CartPole控制任务的功能性能，在施加目标网络规模最小化的进化压力下，实现了低复杂度的6-7神经元解决方案。进化过程在最终解的质量与神经架构搜索效率之间成功取得了平衡。总体而言，我们的发现表明，所提出的MorphoNAS方法能够使用简单的发育规则生长出复杂的特定神经架构，这为自适应且高效的神经架构搜索提供了一条可行的生物学路径。