The chart shows the distribution of publications by all Research.com ranked scientists in the field of Economics and Finance in 2026. The highlighted bar marks where Oliver E. Williamson sits on this spectrum.
This scientist: 259 publications — 77th percentile
77% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 521 publications or more.
The chart shows the D-index (discipline H-index) distribution of Economics and Finance scientists ranked by Research.com in 2026. The highlighted bar marks where Oliver E. Williamson sits on this spectrum.
This scientist: 92 D-Index — 96th percentile
96% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 101 D-Index or more.
Oliver E. Williamson was affiliated with the University of California, Berkeley in the United States. Their primary fields of study encompassed Neuroscience and Engineering, with specific focus on subfields such as Electrical and Electronic Engineering, Cognitive Neuroscience, and Cellular and Molecular Neuroscience.
Their research covered several key topics including Advanced Memory and Neural Computing, Neural dynamics and brain function, and Neuroscience and Neuropharmacology Research. These areas reflect a blend of computational approaches and biological studies related to brain function and neural processes.
Oliver E. Williamson's publication record featured recent research such as "Weight dependence in BCM leads to adjustable synaptic competition," published in 2022 in the Journal of Computational Neuroscience. This paper contributes to the understanding of synaptic plasticity mechanisms and neural competition dynamics.
Their collaborations included frequent co-authors such as Richard Swedberg, Gary S. Becker, James S. Coleman, George A. Akerlof, and Harrison C. White, indicating a diverse network across different academic disciplines.
Oliver E. Williamson also contributed to academic literature through book publications, with a notable work titled Economics and Sociology, published by Princeton University Press in 2020. This suggests an interdisciplinary approach that extended beyond neuroscience and engineering.
Their frequent publication venues included the Journal of Computational Neuroscience, further emphasizing their involvement in computational and theoretical aspects of brain research.
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