2023/07/17 by Elias Najarro, Shyam Sudhakaran, Najarro, Elias +3 · 1 voice · 4 citations
Computer Science · Engineering · Materials Science · #Advanced Memory and Neural Computing #Machine Learning in Materials Science #Neural Networks and Applications #cs.AI #cs.NE
paper · pdf · doi:10.48550/arxiv.2307.08197
openalex publication_date 2023/07/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Biological nervous systems are created in a fundamentally different way than current artificial neural networks. Despite its impressive results in a variety of different domains, deep learning often requires considerable engineering effort to design high-performing neural architectures. By contrast, biological nervous systems are grown through a dynamic self-organizing process. In this paper, we take initial steps toward neural networks that grow through a developmental process that mirrors key properties of embryonic development in biological organisms. The growth process is guided by another neural network, which we call a Neural Developmental Program (NDP) and which operates through local communication alone. We investigate the role of neural growth on different machine learning benchmarks and different optimization methods (evolutionary training, online RL, offline RL, and supervised learning). Additionally, we highlight future research directions and opportunities enabled by having self-organization driving the growth of neural networks.