Milad Lankarany

613 total citations
43 papers, 310 citations indexed

About

Milad Lankarany is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Artificial Intelligence. According to data from OpenAlex, Milad Lankarany has authored 43 papers receiving a total of 310 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Cognitive Neuroscience, 17 papers in Cellular and Molecular Neuroscience and 13 papers in Artificial Intelligence. Recurrent topics in Milad Lankarany's work include Neural dynamics and brain function (20 papers), Neuroscience and Neural Engineering (13 papers) and Neurological disorders and treatments (8 papers). Milad Lankarany is often cited by papers focused on Neural dynamics and brain function (20 papers), Neuroscience and Neural Engineering (13 papers) and Neurological disorders and treatments (8 papers). Milad Lankarany collaborates with scholars based in Canada, United States and Iran. Milad Lankarany's co-authors include Steven A. Prescott, Stéphanie Ratté, Wei‐Ping Zhu, M.N.S. Swamy, Miloš R. Popović, Taro Toyoizumi, Young-Ah Rho, Andrés M. Lozano, Ladan Eskandarian and Amin Mahnam and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Milad Lankarany

36 papers receiving 305 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Milad Lankarany Canada 11 173 143 61 53 45 43 310
Ali Yousefi United States 9 181 1.0× 95 0.7× 30 0.5× 24 0.5× 87 1.9× 28 320
Seif Eldawlatly Egypt 11 360 2.1× 191 1.3× 92 1.5× 31 0.6× 7 0.2× 59 462
Gyöngyi Gaál United States 4 425 2.5× 255 1.8× 22 0.4× 71 1.3× 57 1.3× 4 471
Stephanie Naufel United States 10 284 1.6× 156 1.1× 36 0.6× 112 2.1× 17 0.4× 16 380
Yannick Bornat France 13 324 1.9× 355 2.5× 294 4.8× 114 2.2× 15 0.3× 34 580
Spiros H. Courellis United States 10 224 1.3× 198 1.4× 74 1.2× 24 0.5× 10 0.2× 23 286
Divya Chander United States 5 241 1.4× 179 1.3× 25 0.4× 68 1.3× 10 0.2× 5 453
Jonathan Platkiewicz France 6 244 1.4× 169 1.2× 83 1.4× 44 0.8× 5 0.1× 7 320
Yi Yuan China 12 107 0.6× 50 0.3× 17 0.3× 230 4.3× 34 0.8× 34 386
Gert Van Dijck Belgium 9 137 0.8× 91 0.6× 25 0.4× 39 0.7× 11 0.2× 21 279

Countries citing papers authored by Milad Lankarany

Since Specialization
Citations

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

Fields of papers citing papers by Milad Lankarany

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Milad Lankarany

This figure shows the co-authorship network connecting the top 25 collaborators of Milad Lankarany. A scholar is included among the top collaborators of Milad Lankarany 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 Milad Lankarany. Milad Lankarany 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.
Lankarany, Milad, et al.. (2025). A Novel Computational Model for Integrating Dynamics of Short- and Long-term Synaptic Plasticity During and After Electrical Stimulation. Brain stimulation. 18(1). 248–248. 1 indexed citations
2.
Darmani, Ghazaleh, Hamidreza Ramezanpour, Can Sarica, et al.. (2025). Individualized non-invasive deep brain stimulation of the basal ganglia using transcranial ultrasound stimulation. Nature Communications. 16(1). 2693–2693. 5 indexed citations
3.
Lankarany, Milad, et al.. (2025). AI-Driven Real-Time Monitoring of Cardiovascular Conditions With Wearable Devices: Scoping Review. JMIR mhealth and uhealth. 13. e73846–e73846. 1 indexed citations
4.
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6.
Steiner, Leon A, Artur Vetkas, Jürgen Germann, et al.. (2024). Neural signatures of indirect pathway activity during subthalamic stimulation in Parkinson’s disease. Nature Communications. 15(1). 3130–3130. 15 indexed citations
7.
Lankarany, Milad, et al.. (2023). Uncovering the Origins of Instability in Dynamical Systems: How Can the Attention Mechanism Help?. SHILAP Revista de lepidopterología. 3(2). 214–233. 1 indexed citations
8.
9.
Steiner, Leon A, Suneil K. Kalia, Mojgan Hodaie, et al.. (2023). Modeling Instantaneous Firing Rate of Deep Brain Stimulation Target Neuronal Ensembles in the Basal Ganglia and Thalamus. Neuromodulation Technology at the Neural Interface. 27(3). 464–475. 1 indexed citations
10.
D’Eleuterio, G.M.T., et al.. (2023). Adaptive unscented Kalman filter for neuronal state and parameter estimation. Journal of Computational Neuroscience. 51(2). 223–237. 5 indexed citations
11.
Lankarany, Milad, et al.. (2022). Gradient-Free Neural Network Training via Synaptic-Level Reinforcement Learning. TSpace. 2(2). 185–195. 3 indexed citations
12.
Lankarany, Milad, et al.. (2022). Texture recognition based on multi-sensory integration of proprioceptive and tactile signals. Scientific Reports. 12(1). 21690–21690. 23 indexed citations
13.
Kabir, Muammar, Amin Mahnam, Ladan Eskandarian, et al.. (2021). Modeling and Reproducing Textile Sensor Noise: Implications for Textile-Compatible Signal Processing Algorithms. IEEE Journal of Biomedical and Health Informatics. 26(1). 243–253. 1 indexed citations
14.
Lankarany, Milad, et al.. (2020). Impact of Synaptic Strength on Propagation of Asynchronous Spikes in Biologically Realistic Feed-Forward Neural Network. IEEE Journal of Selected Topics in Signal Processing. 14(4). 646–653. 5 indexed citations
15.
Alizadeh-Meghrazi, Milad, et al.. (2020). Multichannel ECG recording from waist using textile sensors. BioMedical Engineering OnLine. 19(1). 48–48. 34 indexed citations
16.
Lankarany, Milad, Jaime E. Heiss, Ilan Lampl, & Taro Toyoizumi. (2016). Simultaneous Bayesian Estimation of Excitatory and Inhibitory Synaptic Conductances by Exploiting Multiple Recorded Trials. Frontiers in Computational Neuroscience. 10. 110–110. 7 indexed citations
17.
Lankarany, Milad, et al.. (2015). Subthreshold membrane currents confer distinct tuning properties that enable neurons to encode the integral or derivative of their input. Frontiers in Cellular Neuroscience. 8. 452–452. 37 indexed citations
18.
Lankarany, Milad, Wei‐Ping Zhu, M.N.S. Swamy, & Taro Toyoizumi. (2013). Inferring trial-to-trial excitatory and inhibitory synaptic inputs from membrane potential using Gaussian mixture Kalman filtering. Frontiers in Computational Neuroscience. 7. 109–109. 15 indexed citations
19.
Lankarany, Milad, Wei‐Ping Zhu, & M.N.S. Swamy. (2013). Parameter estimation of Hodgkin-Huxley neuronal model using dual extended Kalman filter. 2493–2496. 4 indexed citations
20.
Lankarany, Milad & Alireza Ahmadyfard. (2009). EAR SEGMETATION USING TOPOGRAPHIC LABELS. 186–191.

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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