In this book Peter Robin Hiesinger explores historical and contemporary attempts to understand
the information needed to make biological and artificial neural networks. Developmental
neurobiologists and computer scientists with an interest in artificial intelligence - driven by
the promise and resources of biomedical research on the one hand and by the promise and
advances of computer technology on the other - are trying to understand the fundamental
principles that guide the generation of an intelligent system. Yet though researchers in these
disciplines share a common interest their perspectives and approaches are often quite
different. The book makes the case that the information problem underlies both fields driving
the questions that are driving forward the frontiers and aims to encourage cross-disciplinary
communication and understanding to help both fields make progress. The questions that
challenge researchers in these fields include the following. How does genetic information
unfold during the years-long process of human brain development and can this be a short-cut to
create human-level artificial intelligence? Is the biological brain just messy hardware that
can be improved upon by running learning algorithms in computers? Can artificial intelligence
bypass evolutionary programming of grown networks? These questions are tightly linked and
answering them requires an understanding of how information unfolds algorithmically to generate
functional neural networks. Via a series of closely linked discussions (fictional dialogues
between researchers in different disciplines) and pedagogical seminars the author explores the
different challenges facing researchers working on neural networks their different
perspectives and approaches as well as the common ground and understanding to be found amongst
those sharing an interest in the development of biological brains and artificial intelligent
systems--
