Arindam Basu

4.4k total citations · 2 hit papers
150 papers, 3.1k citations indexed

About

Arindam Basu is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Cognitive Neuroscience. According to data from OpenAlex, Arindam Basu has authored 150 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 128 papers in Electrical and Electronic Engineering, 50 papers in Cellular and Molecular Neuroscience and 49 papers in Cognitive Neuroscience. Recurrent topics in Arindam Basu's work include Advanced Memory and Neural Computing (97 papers), Neuroscience and Neural Engineering (44 papers) and Neural dynamics and brain function (40 papers). Arindam Basu is often cited by papers focused on Advanced Memory and Neural Computing (97 papers), Neuroscience and Neural Engineering (44 papers) and Neural dynamics and brain function (40 papers). Arindam Basu collaborates with scholars based in Singapore, United States and Hong Kong. Arindam Basu's co-authors include P. Hasler, Nripan Mathews, Rohit Abraham John, Mohit Rameshchandra Kulkarni, Enyi Yao, Shaista Hussain, Stephen Brink, Si En Ng, Chao Zhu and Zheng Liu and has published in prestigious journals such as Advanced Materials, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Arindam Basu

146 papers receiving 3.0k citations

Hit Papers

Synergistic Gating of Electro‐Iono‐Photoactive 2D Chalcog... 2018 2026 2020 2023 2018 2021 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Arindam Basu Singapore 29 2.6k 954 741 681 525 150 3.1k
Yunning Li United States 14 3.6k 1.4× 1.5k 1.6× 720 1.0× 709 1.0× 145 0.3× 18 3.8k
Baker Mohammad United Arab Emirates 29 2.3k 0.9× 619 0.6× 296 0.4× 277 0.4× 906 1.7× 237 3.3k
Wenqiang Zhang China 17 4.3k 1.7× 1.5k 1.6× 706 1.0× 1.0k 1.5× 179 0.3× 41 4.6k
Qingtian Zhang China 24 3.9k 1.5× 1.5k 1.6× 732 1.0× 968 1.4× 268 0.5× 72 4.4k
Mohammed A. Zidan Saudi Arabia 22 3.6k 1.4× 1.5k 1.6× 709 1.0× 909 1.3× 184 0.4× 43 4.0k
Ivan Vo United States 6 2.7k 1.0× 1.1k 1.1× 892 1.2× 854 1.3× 101 0.2× 12 2.9k
Jun Sawada United States 8 3.9k 1.5× 1.4k 1.4× 1.3k 1.8× 1.3k 1.9× 139 0.3× 9 4.2k
Peng Yao China 28 5.1k 2.0× 1.8k 1.9× 940 1.3× 1.3k 1.9× 246 0.5× 93 5.5k
Pritish Narayanan United States 24 4.2k 1.6× 1.0k 1.1× 482 0.7× 1.1k 1.6× 259 0.5× 87 4.6k
Bernard Brezzo United States 9 3.9k 1.5× 1.4k 1.5× 1.3k 1.8× 1.3k 2.0× 116 0.2× 11 4.3k

Countries citing papers authored by Arindam Basu

Since Specialization
Citations

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

Fields of papers citing papers by Arindam Basu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Arindam Basu

This figure shows the co-authorship network connecting the top 25 collaborators of Arindam Basu. A scholar is included among the top collaborators of Arindam Basu 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 Arindam Basu. Arindam Basu 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.
Dong, Shuai, Giacomo Pedretti, Xia Sheng, et al.. (2025). Efficient nonlinear function approximation in analog resistive crossbars for recurrent neural networks. Nature Communications. 16(1). 1136–1136. 4 indexed citations
2.
Tay, Wee Peng, et al.. (2025). Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface. Neuromorphic Computing and Engineering. 5(1). 14004–14004. 2 indexed citations
3.
Basu, Arindam, et al.. (2025). Topkima-Former: Low-Energy, Low-Latency Inference for Transformers Using Top-k In-Memory ADC. IEEE Transactions on Circuits and Systems I Regular Papers. 72(6). 2509–2519.
4.
Ng, Si En, et al.. (2024). Halide perovskite photovoltaics for in-sensor reservoir computing. Nano Energy. 129. 109949–109949. 12 indexed citations
5.
Titterton, Alexander, et al.. (2024). Exploiting deep learning accelerators for neuromorphic workloads. SHILAP Revista de lepidopterología. 4(1). 14004–14004. 2 indexed citations
6.
Vishwanath, Sujaya Kumar, Benny Febriansyah, Si En Ng, et al.. (2024). High-performance one-dimensional halide perovskite crossbar memristors and synapses for neuromorphic computing. Materials Horizons. 11(11). 2643–2656. 30 indexed citations
7.
Gopalakrishnan, P. K., Chip-Hong Chang, & Arindam Basu. (2024). HAST: A Hardware-Efficient Spatio-Temporal Correlation Near-Sensor Noise Filter for Dynamic Vision Sensors. IEEE Transactions on Circuits and Systems I Regular Papers. 72(3). 1332–1345.
8.
Basu, Arindam, et al.. (2021). A 51.3-TOPS/W, 134.4-GOPS In-Memory Binary Image Filtering in 65-nm CMOS. IEEE Journal of Solid-State Circuits. 57(1). 323–335. 9 indexed citations
9.
John, Rohit Abraham, Naveen Tiwari, Mohit Rameshchandra Kulkarni, et al.. (2020). Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics. Nature Communications. 11(1). 4030–4030. 95 indexed citations
10.
John, Rohit Abraham, Chao Zhu, Abhijith Surendran, et al.. (2020). Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks. Nature Communications. 11(1). 3211–3211. 52 indexed citations
11.
Gopalakrishnan, P. K., et al.. (2020). ADIC: Anomaly Detection Integrated Circuit in 65-nm CMOS Utilizing Approximate Computing. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 28(12). 2518–2529. 6 indexed citations
12.
Basu, Joydeep, et al.. (2020). A 126 μW Readout Circuit in 65 nm CMOS With Successive Approximation-Based Thresholding for Domain Wall Magnet-Based Random Number Generator. IEEE Sensors Journal. 20(14). 7810–7818. 4 indexed citations
13.
Chua, Yansong, et al.. (2020). HFNet: A CNN Architecture Co-designed for Neuromorphic Hardware With a Crossbar Array of Synapses. Frontiers in Neuroscience. 14. 907–907. 15 indexed citations
14.
Wang, Zheng, et al.. (2019). A 2.86-TOPS/W Current Mirror Cross-Bar-Based Machine-Learning and Physical Unclonable Function Engine For Internet-of-Things Applications. IEEE Transactions on Circuits and Systems I Regular Papers. 66(6). 2240–2252. 21 indexed citations
15.
Yao, Enyi, et al.. (2016). Pulse-based feature extraction for hardware-efficient neural recording systems. 1842–1845. 1 indexed citations
16.
Banerjee, Amitava, et al.. (2015). A current-mode spiking neural classifier with lumped dendritic nonlinearity. 714–717. 9 indexed citations
17.
Brink, Stephen, Stephen Nease, P. Hasler, et al.. (2012). A Learning-Enabled Neuron Array IC Based Upon Transistor Channel Models of Biological Phenomena. IEEE Transactions on Biomedical Circuits and Systems. 7(1). 71–81. 80 indexed citations
18.
Basu, Arindam. (2011). Small-Signal Neural Models and Their Applications. IEEE Transactions on Biomedical Circuits and Systems. 6(1). 64–75. 7 indexed citations
19.
Peng, Sheng-Yu, Gokce Gurun, Christopher M. Twigg, et al.. (2009). A large-scale Reconfigurable Smart Sensory Chip. 2145–2148. 15 indexed citations
20.
Dutt, Aparna, Shyamal Kumar Das, Arindam Basu, et al.. (2007). Identification of neuroanatomical substrates of set-shifting ability: evidence from patients with focal brain lesions. Progress in brain research. 168. 95–104. 20 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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