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. Th is methodology is applied to the field of neural network research, We propose a field-definition based on the present situation, This is don e by letting the data themselves generate a structure, and, with that, define the subdivision of the research field into meaningful subfield s. In order to study the evolution over time, the above ''self-organiz ed'' definition of the present structure is taken as a framework for t he past structure. We explore this evolution by monitoring the interre lations between subfields and by zooming into the internal structure o f each subfield. The overall (''coarse'') structure and the detailed s ubfield maps (''fine structure'') are used for monitoring the dynamica l features of the entire research field. Furthermore, by determining t he positions of the main actors on the map, these structures can also be used to assess the activities of these main actors (universities, f irms, countries, etc.). Finally, we ''reverse'' our approach by analyz ing the developments based on a structure generated in the past. Compa rison of the ''real present'' and the ''present constructed from the p ast'' 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 in to a possible future perspective.