The Self-Assembling Brain

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In this work, Peter Robin Hiesinger delves into the historical and contemporary efforts to grasp the information necessary for developing biological and artificial neural networks. Developmental neurobiologists and computer scientists, motivated by the potential of biomedical research and advancements in computer technology, seek to uncover the fundamental principles that govern intelligent system creation. Despite their shared interests, the two fields often approach problems from divergent perspectives. The book posits that "the information problem" is central to both disciplines, shaping the critical questions that propel research forward. It aims to foster cross-disciplinary dialogue to facilitate progress in both areas. Key questions include how genetic information unfolds during human brain development and whether this knowledge could expedite the creation of human-level artificial intelligence. It also examines whether the biological brain is merely imperfect hardware that can be enhanced through learning algorithms in computers and if artificial intelligence can surpass the evolutionary programming of "grown" networks. Addressing these interconnected questions necessitates an understanding of how information unfolds algorithmically to create functional neural networks. Through fictional dialogues and pedagogical seminars, the author highlights the unique challenges faced by researchers, their varying perspectives, and