How does a neural network become a brain? While neurobiologists investigate how nature accomplishes this feat, computer scientists interested in AI strive to achieve this through technology. The Self-Assembling Brain tells the stories of both fields, exploring the historical and modern approaches taken by the scientists pursuing answers to the quandary: What information is necessary to make an intelligent neural network?



As Peter Robin Hiesinger argues, 'the information problem' underlies both fields. How does genetic information unfold during the process of human brain development—and is there a quicker path to creating human-level artificial intelligence? Is the biological brain just messy hardware, which scientists can improve upon by running learning algorithms on computers? Can AI bypass the evolutionary programming of 'grown' networks? Hiesinger explores these tightly linked questions, highlighting the challenges facing scientists, their different disciplinary perspectives, and the common ground shared by those interested in the development of biological brains and AI systems. Hiesinger contends that the information content of biological and artificial neural networks must unfold in an algorithmic process requiring time and energy. There is no genome and no blueprint that depicts the final product. The self-assembling brain knows no shortcuts.