Dean V. Buonomano
- Cognitive Neuroscience top 0.2%
- Cellular and Molecular Neuroscience top 0.5%
- Experimental and Cognitive Psychology top 1%
- Electrical and Electronic Engineering top 5%
- Artificial Intelligence top 2%
- Co-authors
- Michael M. MerzenichMichael D. MaukUma R. KarmarkarWolfgang MaassRodrigo LajeJoseph J. PatonJohn H. ByrneAnubhuti Goel
- Topics
- Neural dynamics and brain function (63 papers)Neuroscience and Music Perception (29 papers)Advanced Memory and Neural Computing (21 papers)
- Partner nations
- United StatesBulgariaArgentina
In The Last Decade
Dean V. Buonomano
76 papers receiving 7.3k citations
Hit Papers
Peers
Comparison fields: 5 of 169
- Cognitive Neuroscience 6.1k
- Cellular and Molecular Neuroscience 2.2k
- Experimental and Cognitive Psychology 1.2k
- Electrical and Electronic Engineering 1.2k
- Artificial Intelligence 747
Countries citing papers authored by Dean V. Buonomano
This map shows the geographic impact of Dean V. Buonomano'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 Dean V. Buonomano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dean V. Buonomano more than expected).
Fields of papers citing papers by Dean V. Buonomano
This network shows the impact of papers produced by Dean V. Buonomano. 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 Dean V. Buonomano. The network helps show where Dean V. Buonomano may publish in the future.
Co-authorship network of co-authors of Dean V. Buonomano
This figure shows the co-authorship network connecting the top 25 collaborators of Dean V. Buonomano. A scholar is included among the top collaborators of Dean V. Buonomano based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dean V. Buonomano. Dean V. Buonomano is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 9 | |
| 5 | 7 | |
| 6 | 8 | |
| 7 | 10 | |
| 8 | 29 | |
| 9 | 57 | |
| 10 | The Neural Basis of Timing: Distributed Mechanisms for Diverse Functionsbreakdown → | 226 |
| 11 | 4 | |
| 12 | 3 | |
| 13 | 18 | |
| 14 | 298 | |
| 15 | 26 | |
| 16 | State-dependent computations: spatiotemporal processing in cortical networksbreakdown → | 628 |
| 17 | 74 | |
| 18 | 140 | |
| 19 | 16 | |
| 20 | 46 |
About Dean V. Buonomano
Dean V. Buonomano is a scholar working on Cognitive Neuroscience, Developmental Biology and Cellular and Molecular Neuroscience, having authored 77 papers that have together received 7.5k indexed citations. Recurring topics across this work include Neural dynamics and brain function (63 papers), Neuroscience and Music Perception (29 papers) and Advanced Memory and Neural Computing (21 papers). The work is most often cited by research in Cognitive Neuroscience (6.1k citations), Cellular and Molecular Neuroscience (2.2k citations) and Developmental Biology (200 citations). Dean V. Buonomano has collaborated with scholars based in United States, Bulgaria and Argentina. Frequent co-authors include Michael M. Merzenich, Michael D. Mauk, Uma R. Karmarkar, Wolfgang Maass, Rodrigo Laje, Joseph J. Paton, John H. Byrne, Anubhuti Goel, Vishwa Goudar and Henry W. Mahncke. Their work appears in journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.
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.