Anirvan S. Nandy

1.3k total citations
29 papers, 844 citations indexed

About

Anirvan S. Nandy is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Cellular and Molecular Neuroscience. According to data from OpenAlex, Anirvan S. Nandy has authored 29 papers receiving a total of 844 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Cognitive Neuroscience, 5 papers in Computer Vision and Pattern Recognition and 4 papers in Cellular and Molecular Neuroscience. Recurrent topics in Anirvan S. Nandy's work include Visual perception and processing mechanisms (18 papers), Neural dynamics and brain function (15 papers) and Neural and Behavioral Psychology Studies (4 papers). Anirvan S. Nandy is often cited by papers focused on Visual perception and processing mechanisms (18 papers), Neural dynamics and brain function (15 papers) and Neural and Behavioral Psychology Studies (4 papers). Anirvan S. Nandy collaborates with scholars based in United States, Australia and Italy. Anirvan S. Nandy's co-authors include Bosco S. Tjan, John H. Reynolds, Thomas F. Denson, William C. Pedersen, Jonathan J. Nassi, MiYoung Kwon, Jude F. Mitchell, Tatyana O. Sharpee, Monika P. Jadi and Keith B. Maddox and has published in prestigious journals such as Nature Communications, Neuron and Journal of Neuroscience.

In The Last Decade

Anirvan S. Nandy

27 papers receiving 825 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Anirvan S. Nandy United States 12 656 143 122 95 83 29 844
Claudine Habak Canada 13 722 1.1× 121 0.8× 136 1.1× 73 0.8× 25 0.3× 30 889
Isabel Arend Israel 16 620 0.9× 203 1.4× 89 0.7× 55 0.6× 20 0.2× 46 836
Min Bao China 15 624 1.0× 102 0.7× 65 0.5× 74 0.8× 22 0.3× 54 751
Yuhan Chen United States 18 696 1.1× 183 1.3× 90 0.7× 60 0.6× 53 0.6× 48 938
Linda Henriksson Finland 18 837 1.3× 120 0.8× 187 1.5× 38 0.4× 15 0.2× 35 1.0k
Christopher P. Said United States 12 1.1k 1.7× 620 4.3× 222 1.8× 58 0.6× 73 0.9× 16 1.3k
Jiefeng Jiang United States 22 975 1.5× 245 1.7× 80 0.7× 52 0.5× 42 0.5× 50 1.3k
Eunice Yang United States 8 1.1k 1.7× 251 1.8× 190 1.6× 50 0.5× 14 0.2× 11 1.3k
Marcus Rothkirch Germany 17 700 1.1× 222 1.6× 66 0.5× 56 0.6× 20 0.2× 34 904
Andrew M. Herbert United States 15 571 0.9× 182 1.3× 157 1.3× 29 0.3× 17 0.2× 32 778

Countries citing papers authored by Anirvan S. Nandy

Since Specialization
Citations

This map shows the geographic impact of Anirvan S. Nandy'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 Anirvan S. Nandy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anirvan S. Nandy more than expected).

Fields of papers citing papers by Anirvan S. Nandy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anirvan S. Nandy. 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 Anirvan S. Nandy. The network helps show where Anirvan S. Nandy may publish in the future.

Co-authorship network of co-authors of Anirvan S. Nandy

This figure shows the co-authorship network connecting the top 25 collaborators of Anirvan S. Nandy. A scholar is included among the top collaborators of Anirvan S. Nandy 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 Anirvan S. Nandy. Anirvan S. Nandy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Joyce, Mary Kate P., Fenna M. Krienen, Jude F. Mitchell, et al.. (2025). Higher dopamine D1 receptor expression in prefrontal parvalbumin neurons underlies higher distractibility in marmosets versus macaques. Communications Biology. 8(1). 974–974. 2 indexed citations
2.
Meisner, Olivia C., Olga Dal Monte, Nicholas Fagan, Anirvan S. Nandy, & Steve W. C. Chang. (2025). Oxytocin in the Amygdala Sustains Prosocial Behavior via State-Dependent Amygdala–Prefrontal Modulation. Journal of Neuroscience. 45(36). e2416242025–e2416242025.
3.
Meisner, Olivia C., Weikang Shi, Amrita R. Nair, et al.. (2025). Diverse and flexible strategies enable successful cooperation in marmoset dyads. Current Biology. 35(18). 4509–4521.e5.
4.
Morton, Mitchell P., et al.. (2024). Widespread receptive field remapping in early primate visual cortex. Cell Reports. 43(8). 114557–114557. 3 indexed citations
5.
Das, Anirban, et al.. (2024). Brain-state mediated modulation of inter-laminar dependencies in visual cortex. Nature Communications. 15(1). 5105–5105. 6 indexed citations
6.
Morton, Mitchell P., et al.. (2024). Spatial context non-uniformly modulates inter-laminar information flow in the primary visual cortex. Neuron. 112(24). 4081–4095.e5. 1 indexed citations
7.
Morton, Mitchell J. L., et al.. (2024). Geometry of anisotropic contextual interactions in the visual cortex places fundamental limits on spatial vision.. Journal of Vision. 24(10). 1432–1432. 1 indexed citations
8.
Nandy, Anirvan S., et al.. (2023). Laminar compartmentalization of attention modulation in area V4 aligns with the demands of visual processing hierarchy in the cortex. Scientific Reports. 13(1). 19558–19558. 2 indexed citations
9.
Shi, Weikang, et al.. (2023). The orbitofrontal cortex: A goal-directed cognitive map framework for social and non-social behaviors. Neurobiology of Learning and Memory. 203. 107793–107793. 10 indexed citations
10.
Nandy, Anirvan S., Jonathan J. Nassi, Monika P. Jadi, & John H. Reynolds. (2019). Optogenetically induced low-frequency correlations impair perception. eLife. 8. 20 indexed citations
11.
Nandy, Anirvan S., Jude F. Mitchell, Monika P. Jadi, & John H. Reynolds. (2016). Neurons in Macaque Area V4 Are Tuned for Complex Spatio-Temporal Patterns. Neuron. 91(4). 920–930. 15 indexed citations
12.
Wallace, Julian, et al.. (2013). Crowding during restricted and free viewing. Vision Research. 84. 50–59. 20 indexed citations
13.
Kwon, MiYoung, Anirvan S. Nandy, & Bosco S. Tjan. (2013). Rapid and Persistent Adaptability of Human Oculomotor Control in Response to Simulated Central Vision Loss. Current Biology. 23(17). 1663–1669. 73 indexed citations
14.
Nandy, Anirvan S. & Bosco S. Tjan. (2012). Saccade-confounded image statistics explain visual crowding. Nature Neuroscience. 15(3). 463–469. 97 indexed citations
15.
Tjan, Bosco S., MiYoung Kwon, & Anirvan S. Nandy. (2011). Changes in oculomotor behavior induced by a simulated central scotoma. Journal of Vision. 11(11). 484–484. 1 indexed citations
16.
Wallace, Julian, et al.. (2011). Peripheral crowding with unlimited viewing time. Journal of Vision. 11(11). 1154–1154. 1 indexed citations
17.
Denson, Thomas F., et al.. (2008). The Angry Brain: Neural Correlates of Anger, Angry Rumination, and Aggressive Personality. Journal of Cognitive Neuroscience. 21(4). 734–744. 181 indexed citations
18.
Nandy, Anirvan S. & Bosco S. Tjan. (2008). Efficient integration across spatial frequencies for letter identification in foveal and peripheral vision. Journal of Vision. 8(13). 3–3. 16 indexed citations
19.
Nandy, Anirvan S. & Bosco S. Tjan. (2007). The nature of letter crowding as revealed by first- and second-order classification images. Journal of Vision. 7(2). 5–5. 80 indexed citations
20.
Tjan, Bosco S. & Anirvan S. Nandy. (2006). Classification images with uncertainty. Journal of Vision. 6(4). 8–8. 44 indexed citations

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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