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Biological nervous systems are created in a fundamentally different way thancurrent artificial neural networks. Despite its impressive results in a varietyof different domains, deep learning often requires considerable engineeringeffort to design high-performing neural architectures. By contrast, biologicalnervous systems are grown through a dynamic self-organizing process. In thispaper, we take initial steps toward neural networks that grow through adevelopmental process that mirrors key properties of embryonic development inbiological organisms. The growth process is guided by another neural network,which we call a Neural Developmental Program (NDP) and which operates throughlocal communication alone. We investigate the role of neural growth ondifferent machine learning benchmarks and different optimization methods(evolutionary training, online RL, offline RL, and supervised learning).Additionally, we highlight future research directions and opportunities enabledby having self-organization driving the growth of neural networks.

Towards Self-Assembling Artificial Neural Networks through Neural Developmental Programs