Aditya Gilra

645 total citations
10 papers, 279 citations indexed

About

Aditya Gilra is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Aditya Gilra has authored 10 papers receiving a total of 279 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Cognitive Neuroscience, 6 papers in Artificial Intelligence and 4 papers in Electrical and Electronic Engineering. Recurrent topics in Aditya Gilra's work include Neural dynamics and brain function (7 papers), Neural Networks and Applications (4 papers) and Neural Networks and Reservoir Computing (4 papers). Aditya Gilra is often cited by papers focused on Neural dynamics and brain function (7 papers), Neural Networks and Applications (4 papers) and Neural Networks and Reservoir Computing (4 papers). Aditya Gilra collaborates with scholars based in United Kingdom, Netherlands and Switzerland. Aditya Gilra's co-authors include Rajeev Rikhye, Michael M. Halassa, Wulfram Gerstner, Raoul-Martin Memmesheimer, Upinder S. Bhalla, Eleni Vasilaki, S. K. Manhas, Partha Pratim Roy, M.M. De Souza and Nicholas J. Cole and has published in prestigious journals such as Physical Review Letters, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Aditya Gilra

10 papers receiving 277 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Aditya Gilra United Kingdom	6	196	79	69	64	20	10	279
Christopher J. Cueva United States	5	303 1.5×	79 1.0×	59 0.9×	68 1.1×	14 0.7×	7	325
Andrei Belitski Germany	6	439 2.2×	36 0.5×	214 3.1×	60 0.9×	20 1.0×	7	489
João D. Semedo Portugal	7	362 1.8×	31 0.4×	149 2.2×	21 0.3×	24 1.2×	10	395
Julien Vitay Germany	9	245 1.3×	49 0.6×	102 1.5×	62 1.0×	6 0.3×	20	341
Cristina Savin United States	12	286 1.5×	66 0.8×	150 2.2×	104 1.6×	29 1.4×	33	362
Petr Maršálek Czechia	8	309 1.6×	46 0.6×	105 1.5×	54 0.8×	69 3.5×	27	401
Benjamin Dann Germany	9	330 1.7×	24 0.3×	105 1.5×	22 0.3×	17 0.8×	10	382
Michael E. Rule United Kingdom	9	307 1.6×	43 0.5×	179 2.6×	49 0.8×	17 0.8×	17	356
Mircea I. Chelaru United States	10	313 1.6×	26 0.3×	220 3.2×	45 0.7×	36 1.8×	19	410
Yaser Merrikhi Iran	8	299 1.5×	20 0.3×	59 0.9×	35 0.5×	51 2.5×	22	359

Countries citing papers authored by Aditya Gilra

Since Specialization

Citations

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

Fields of papers citing papers by Aditya Gilra

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aditya Gilra. 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 Aditya Gilra. The network helps show where Aditya Gilra may publish in the future.

Co-authorship network of co-authors of Aditya Gilra

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

All Works

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10 of 10 papers shown

Cole, Nicholas J., et al.. (2024). Prediction-error signals in anterior cingulate cortex drive task-switching. Nature Communications. 15(1). 7088–7088. 5 indexed citations

Saal, Hannes P., et al.. (2023). Modelling novelty detection in the thalamocortical loop. PLoS Computational Biology. 19(5). e1009616–e1009616. 2 indexed citations

Manhas, S. K., et al.. (2022). Reservoir Computing for Temporal Data Classification Using a Dynamic Solid Electrolyte ZnO Thin Film Transistor. SHILAP Revista de lepidopterología. 3. 13 indexed citations

Gilra, Aditya, et al.. (2020). Dynamical Learning of Dynamics. Physical Review Letters. 125(8). 88103–88103. 36 indexed citations

Rikhye, Rajeev, Aditya Gilra, & Michael M. Halassa. (2018). Thalamic regulation of switching between cortical representations enables cognitive flexibility. Nature Neuroscience. 21(12). 1753–1763. 162 indexed citations

Gilra, Aditya & Wulfram Gerstner. (2017). Non-linear motor control by local learning in spiking neural networks. International Conference on Machine Learning. 1768–1777. 1 indexed citations

Gilra, Aditya & Wulfram Gerstner. (2017). Predicting non-linear dynamics: a stable local learning scheme for recurrent spiking neural networks.. arXiv (Cornell University). 5 indexed citations

Gilra, Aditya & Wulfram Gerstner. (2017). Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network. eLife. 6. 45 indexed citations

Gilra, Aditya & Upinder S. Bhalla. (2015). Bulbar Microcircuit Model Predicts Connectivity and Roles of Interneurons in Odor Coding. PLoS ONE. 10(5). e0098045–e0098045. 9 indexed citations

10.

Gilra, Aditya, Vandna Gokhroo, & C. S. Unnikrishnan. (2007). New tests and clarification of some conceptual issues in the superposition of monochromatic light fields. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6664. 66640N–66640N. 1 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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