Marcella Noorman

9 papers receiving 234 citations

Marcella Noorman's Hit Papers

A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection 2021 · 197 citations
1970+1+3Years since publication50100150

Peers

Marcella Noorman
Comparison fields: 5 of 73
  • Cellular and Molecular Neuroscience 163
  • Endocrine and Autonomic Systems 28
  • Genetics 91
  • Cognitive Neuroscience 52
  • Ecology, Evolution, Behavior and Systematics 51
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Countries citing papers authored by Marcella Noorman

Since Specialization
Citations

This map shows the geographic impact of Marcella Noorman's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marcella Noorman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marcella Noorman more than expected).

Fields of papers citing papers by Marcella Noorman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marcella Noorman. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marcella Noorman. The network helps show where Marcella Noorman may publish in the future.

Co-authors

The 19 scholars most cited alongside Marcella Noorman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Marcella Noorman Line = papers co-authored together Marcella Noorman links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1
A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection
Hit paper breakdown →
2021197
2 201511
3 20179
4 20245
5 20155
6 20194
7 20193
8
Sensitivity analysis in poro-elastic and poro-visco-elastic models
20171
9
PORO-VISCO-ELASTIC MODELS IN BIOMECHANICS: SENSITIVITY ANALYSIS
20191

About Marcella Noorman

Marcella Noorman is a scholar working on Computational Theory and Mathematics, Molecular Biology, Numerical Analysis, Pathology and Forensic Medicine and Cellular and Molecular Neuroscience, having authored 9 papers that have together received 236 indexed citations. Recurring topics across this work include Advanced Mathematical Modeling in Engineering (2 papers), Numerical methods for differential equations (1 paper), Diet, Metabolism, and Disease (1 paper), Alcohol Consumption and Health Effects (1 paper), Neural Networks and Applications (1 paper), Advanced Numerical Methods in Computational Mathematics (1 paper), Mathematical Biology Tumor Growth (1 paper) and Nonlinear Partial Differential Equations (1 paper). The work is most often cited by research in Cellular and Molecular Neuroscience (163 citations), Endocrine and Autonomic Systems (28 citations), Genetics (91 citations), Cognitive Neuroscience (52 citations) and Ecology, Evolution, Behavior and Systematics (51 citations). Marcella Noorman has collaborated with scholars based in United States. Frequent co-authors include Vivek Jayaraman, Ann M. Hermundstad, Brad K. Hulse, Gerald M. Rubin, Ruchi Parekh, Hannah Haberkern, Shin-ya Takemura, Marisa Dreher, Tanya Wolff and Romain Franconville. Their work appears in journals such as eLife, Mathematical Control and Related Fields, Mathematical Biosciences & Engineering, Nature Neuroscience and Quarterly of Applied Mathematics.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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