Monitoring scientific developments from a dynamic perspective: Self‐organized structuring to map neural network research

With the help of bibliometric mapping techniques, we have developed a methodology of “self-organized” structuring of scientific fields. This methodology is applied to the field of neural network research. We propose a field-definition based on the present situation. This is done by letting the data themselves generate a structure, and, with that, define the subdivision of the research field into meaningful subfields. In order to study the evolution over time, the above “self-organized” definition of the present structure is taken as a framework for the past structure. We explore this evolution by monitoring the interrelations between subfields and by zooming into the internal structure of each subfield. The overall (“coarse”) structure and the detailed subfield maps (“fine structure”) are used for monitoring the dynamical features of the entire research field. Furthermore, by determining the positions of the main actors on the map, these structures can also be used to assess the activities of these main actors (universities, firms, countries, etc.). Finally, we “reverse” our approach by analyzing the developments based on a structure generated in the past. Comparison of the “real present” and the “present constructed from the past” may provide new insight into successful, as well as unsuccessful, patterns and “trajectories” of developments. Thus, we explore the potential of our method to put the observed “actual” developments into a possible future perspective. © 1998 John Wiley & Sons, Inc.