MorphoNAS: Embryogenic Neural Architecture Search Through Morphogen-Guided Development

Researchers Mykola Glybovets and Sergii Medvid have introduced MorphoNAS, a groundbreaking approach to Neural Architecture Search (NAS) that draws direct inspiration from biological development. Unlike traditional NAS methods that rely heavily on manual engineering or brute-force computation, MorphoNAS uses a 'genome' that encodes simple rules for morphogen dynamics and cellular development. These local chemical interactions, inspired by reaction-diffusion systems and the Free Energy Principle, guide a single progenitor cell to self-organize into a complete, complex neural network. The process is deterministic, meaning the same genome always produces the same architecture.

In their evolutionary experiments, the team demonstrated MorphoNAS's capabilities in two key areas. First, in structural targeting, the system successfully found genomes that could generate predefined random graph configurations containing 8 to 31 nodes. Second, when applied to the classic CartPole reinforcement learning control task with an evolutionary pressure for minimal size, MorphoNAS discovered highly efficient solutions using only 6 or 7 neurons. The research, published in *Kibernetyka i Systemnyj Analiz*, shows the system effectively balances solution quality with search efficiency.

This embryogenic approach represents a significant conceptual shift in automated AI design. By mimicking how biological brains develop from compact genetic blueprints, MorphoNAS points toward a future where AI systems can be 'grown' to fit specific tasks using simple, scalable rules rather than being manually architected. It bridges the gap between neuroscience-inspired computing and practical engineering, offering a potentially more natural and adaptive route to discovering novel, efficient neural network topologies.