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Abstract:
This thesis presents a bibliometric analysis of the field of computational neuroscience. It aims to map the growth, collaboration patterns, and topic evolution
within computational neuroscience. Publication data from 1980 to 2023 from
the German Competence Centre for Scientometrics (KB) are examined together
with the corresponding references and author details. A co-authorship network
is constructed to explore collaborative patterns, and a co-citation network analysis is conducted to uncover topics and influential references. For further topic
identification, Latent Dirichlet Allocation (LDA), a Natural Language Processing
technique, is applied to analyze titles and abstracts. The term “computational
neuroscience” emerged as a literature keyword in 1991. The field is shown to be
increasingly interdisciplinary. There is significant collaboration within computational neuroscience. Over time, there has been a trend towards increased global
involvement. However, the collaboration network remains fragmented. Authors
affiliated with the United States and Germany have predominantly contributed.
Both foundational and contemporary works are central to the field. Shifts in
research focus are identified. The early prominence of cellular neuroscience has
been accompanied by a consistent relevance of systems neural modeling and circuit dynamics, along with a dominance of cognitive theories in the literature.
The results of this study provide a better understanding of computational neuroscience’s focus areas and the past and current research landscapes.
