Argha Mondal

1.4k total citations
34 papers, 943 citations indexed

About

Argha Mondal is a scholar working on Statistical and Nonlinear Physics, Cognitive Neuroscience and Computer Networks and Communications. According to data from OpenAlex, Argha Mondal has authored 34 papers receiving a total of 943 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Statistical and Nonlinear Physics, 25 papers in Cognitive Neuroscience and 24 papers in Computer Networks and Communications. Recurrent topics in Argha Mondal's work include stochastic dynamics and bifurcation (28 papers), Neural dynamics and brain function (25 papers) and Nonlinear Dynamics and Pattern Formation (22 papers). Argha Mondal is often cited by papers focused on stochastic dynamics and bifurcation (28 papers), Neural dynamics and brain function (25 papers) and Nonlinear Dynamics and Pattern Formation (22 papers). Argha Mondal collaborates with scholars based in India, United Kingdom and United States. Argha Mondal's co-authors include Chris G. Antonopoulos, Ian A. Cooper, Ranjit Kumar Upadhyay, Wondimu Teka, Jun Ma, M. A. Aziz-Alaoui, Chittaranjan Hens, Éva Kaslik, Mohammad Ali Khan and Swarup Poria and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Neural Networks.

In The Last Decade

Argha Mondal

33 papers receiving 916 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Argha Mondal India 12 455 404 235 233 170 34 943
Chris G. Antonopoulos United Kingdom 17 399 0.9× 430 1.1× 161 0.7× 296 1.3× 141 0.8× 57 1.2k
Antoine Allard Canada 19 409 0.9× 819 2.0× 91 0.4× 110 0.5× 213 1.3× 64 1.5k
Clara Granell Spain 13 587 1.3× 1.4k 3.4× 131 0.6× 272 1.2× 243 1.4× 24 1.9k
Yuexi Peng China 19 452 1.0× 919 2.3× 277 1.2× 360 1.5× 153 0.9× 41 1.7k
L.H.A. Monteiro Brazil 19 211 0.5× 350 0.9× 76 0.3× 267 1.1× 249 1.5× 116 1.1k
Mitja Slavinec Slovenia 13 156 0.3× 297 0.7× 168 0.7× 264 1.1× 42 0.2× 26 674
Yuliya N. Kyrychko United Kingdom 20 397 0.9× 239 0.6× 43 0.2× 275 1.2× 396 2.3× 48 1.1k
Pietro De Lellis Italy 20 150 0.3× 761 1.9× 131 0.6× 1.7k 7.3× 125 0.7× 90 2.4k
Juliette Stehlé France 7 377 0.8× 1.0k 2.5× 27 0.1× 210 0.9× 60 0.4× 8 1.5k
Benyun Shi China 16 193 0.4× 79 0.2× 42 0.2× 63 0.3× 160 0.9× 74 807

Countries citing papers authored by Argha Mondal

Since Specialization
Citations

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

Fields of papers citing papers by Argha Mondal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Argha Mondal

This figure shows the co-authorship network connecting the top 25 collaborators of Argha Mondal. A scholar is included among the top collaborators of Argha Mondal 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 Argha Mondal. Argha Mondal 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.
Kaslik, Éva, et al.. (2025). Simulating neuronal dynamics in fractional adaptive exponential integrate-and-fire models. Fractional Calculus and Applied Analysis. 28(2). 529–558.
2.
Mondal, Argha, et al.. (2024). Emergent dynamics in fractional-order Wilson–Cowan neural network systems. Chaos Solitons & Fractals. 181. 114687–114687. 3 indexed citations
3.
Mondal, Argha, et al.. (2023). Diverse electrical responses in a network of fractional-order conductance-based excitable Morris-Lecar systems. Scientific Reports. 13(1). 8215–8215. 7 indexed citations
4.
Aziz-Alaoui, M. A., et al.. (2023). Non-Trivial Dynamics in the FizHugh–Rinzel Model and Non-Homogeneous Oscillatory-Excitable Reaction-Diffusions Systems. Biology. 12(7). 918–918. 8 indexed citations
5.
Mahato, Sanat Kumar, et al.. (2023). Emergence of diverse dynamical responses in a fractional-order slow–fast pest–predator model. Nonlinear Dynamics. 111(9). 8821–8836. 4 indexed citations
6.
Mondal, Argha, et al.. (2022). The generation of diverse traveling pulses and its solution scheme in an excitable slow-fast dynamics. Chaos An Interdisciplinary Journal of Nonlinear Science. 32(8). 83121–83121. 1 indexed citations
7.
Mondal, Argha, et al.. (2022). Emergence of Canard induced mixed mode oscillations in a slow–fast dynamics of a biophysical excitable model. Chaos Solitons & Fractals. 164. 112669–112669. 10 indexed citations
8.
Upadhyay, Ranjit Kumar, et al.. (2022). Emergence of Turing patterns and dynamic visualization in excitable neuron model. Applied Mathematics and Computation. 423. 127010–127010. 8 indexed citations
9.
Mukherjee, Madhurima, et al.. (2022). Traveling pulses and its wave solution scheme in a diffusively coupled 2D Hindmarsh-Rose excitable systems. Nonlinear Dynamics. 111(7). 6745–6755. 2 indexed citations
10.
Cooper, Ian A., Argha Mondal, Chris G. Antonopoulos, & Arindam Mishra. (2022). Dynamical analysis of the infection status in diverse communities due to COVID-19 using a modified SIR model. Nonlinear Dynamics. 109(1). 19–32. 4 indexed citations
11.
Mondal, Argha, et al.. (2020). Emergence of bursting in a network of memory dependent excitable and spiking leech-heart neurons. Journal of The Royal Society Interface. 17(167). 20190859–20190859. 8 indexed citations
12.
Mondal, Argha, et al.. (2019). Firing activities of a fractional-order FitzHugh-Rinzel bursting neuron model and its coupled dynamics. Scientific Reports. 9(1). 15721–15721. 57 indexed citations
13.
Mondal, Argha, et al.. (2019). Bifurcation analysis and diverse firing activities of a modified excitable neuron model. Cognitive Neurodynamics. 13(4). 393–407. 74 indexed citations
14.
Mondal, Argha, et al.. (2019). Diffusion dynamics of a conductance-based neuronal population. Physical review. E. 99(4). 42307–42307. 13 indexed citations
15.
Upadhyay, Ranjit Kumar, Argha Mondal, & Wondimu Teka. (2017). Mixed Mode Oscillations and Synchronous Activity in Noise Induced Modified Morris–Lecar Neural System. International Journal of Bifurcation and Chaos. 27(5). 1730019–1730019. 28 indexed citations
16.
Upadhyay, Ranjit Kumar, Argha Mondal, & M. A. Aziz-Alaoui. (2017). Synchronization analysis through coupling mechanism in realistic neural models. Applied Mathematical Modelling. 44. 557–575. 2 indexed citations
17.
Teka, Wondimu, Ranjit Kumar Upadhyay, & Argha Mondal. (2017). Fractional-order leaky integrate-and-fire model with long-term memory and power law dynamics. Neural Networks. 93. 110–125. 40 indexed citations
18.
Teka, Wondimu, Ranjit Kumar Upadhyay, & Argha Mondal. (2017). Spiking and bursting patterns of fractional-order Izhikevich model. Communications in Nonlinear Science and Numerical Simulation. 56. 161–176. 52 indexed citations
19.
Upadhyay, Ranjit Kumar & Argha Mondal. (2015). Dynamics of fractional order modified Morris-Lecar neural model. SHILAP Revista de lepidopterología. 10 indexed citations
20.
Khan, Mohammad Ali, Argha Mondal, & Swarup Poria. (2011). Three control strategies for unified chaotic system. 16(2). 597–608. 7 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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