Chung‐Chuan Lo

5.1k total citations
76 papers, 2.4k citations indexed

About

Chung‐Chuan Lo is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Cognitive Neuroscience. According to data from OpenAlex, Chung‐Chuan Lo has authored 76 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Electrical and Electronic Engineering, 26 papers in Cellular and Molecular Neuroscience and 23 papers in Cognitive Neuroscience. Recurrent topics in Chung‐Chuan Lo's work include Advanced Memory and Neural Computing (35 papers), Neural dynamics and brain function (17 papers) and Neurobiology and Insect Physiology Research (16 papers). Chung‐Chuan Lo is often cited by papers focused on Advanced Memory and Neural Computing (35 papers), Neural dynamics and brain function (17 papers) and Neurobiology and Insect Physiology Research (16 papers). Chung‐Chuan Lo collaborates with scholars based in Taiwan, United States and China. Chung‐Chuan Lo's co-authors include Xiao‐Jing Wang, Kea‐Tiong Tang, Ren-Shuo Liu, Meng‐Fan Chang, Thomas Penzel, Chih-Cheng Hsieh, Plamen Ch. Ivanov, H. Eugene Stanley, Tzu-Hsiang Hsu and Sheng-Po Huang and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Chung‐Chuan Lo

67 papers receiving 2.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chung‐Chuan Lo Taiwan 21 1.0k 983 472 236 184 76 2.4k
Steve K. Esser United States 15 904 0.9× 3.2k 3.3× 966 2.0× 393 1.7× 472 2.6× 16 4.1k
Sylvie Renaud France 18 1.3k 1.3× 1.0k 1.0× 953 2.0× 345 1.5× 35 0.2× 58 2.2k
William W. Lytton United States 30 522 0.5× 2.3k 2.4× 1.8k 3.9× 259 1.1× 36 0.2× 146 3.5k
Dan F. M. Goodman France 19 1.1k 1.1× 2.0k 2.0× 1.2k 2.5× 415 1.8× 28 0.2× 43 2.6k
Ertuğrul Başar Türkiye 53 9.4k 9.0× 1.8k 1.8× 443 0.9× 425 1.8× 25 0.1× 291 12.1k
Patrícia Figueiredo Portugal 27 455 0.4× 1.8k 1.8× 236 0.5× 27 0.1× 23 0.1× 115 2.8k
Günther Palm Germany 23 256 0.2× 1.0k 1.0× 384 0.8× 675 2.9× 9 0.0× 113 2.1k
Tara Julia Hamilton Australia 22 1.9k 1.8× 1.4k 1.4× 1.0k 2.2× 576 2.4× 11 0.1× 114 2.8k
Eric Shea‐Brown United States 27 360 0.3× 2.0k 2.0× 1.1k 2.2× 234 1.0× 44 0.2× 76 2.4k
Anthony N. Burkitt Australia 31 689 0.7× 1.8k 1.9× 1.3k 2.7× 106 0.4× 11 0.1× 168 2.6k

Countries citing papers authored by Chung‐Chuan Lo

Since Specialization
Citations

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

Fields of papers citing papers by Chung‐Chuan Lo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chung‐Chuan Lo. 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 Chung‐Chuan Lo. The network helps show where Chung‐Chuan Lo may publish in the future.

Co-authorship network of co-authors of Chung‐Chuan Lo

This figure shows the co-authorship network connecting the top 25 collaborators of Chung‐Chuan Lo. A scholar is included among the top collaborators of Chung‐Chuan Lo 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 Chung‐Chuan Lo. Chung‐Chuan Lo 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.
2.
Wang, Yimei, Peng Chen, Sheng‐Han Kuo, et al.. (2025). The cerebellum shapes motions by encoding motor frequencies with precision and cross-individual uniformity. Nature Biomedical Engineering. 9(11). 1952–1971.
3.
Wu, Ping-Chun, Win-San Khwa, Ashwin Sanjay Lele, et al.. (2025). A Microscaling Multi-Mode Gain-Cell Computing-in-Memory Macro for Advanced AI Edge Device. IEEE Journal of Solid-State Circuits. 61(1). 211–224.
4.
5.
Lo, Chung‐Chuan, et al.. (2024). Attractiveness versus stickiness: Behavioural preferences of Drosophila melanogaster with competing visual stimuli. Journal of Insect Physiology. 159. 104716–104716. 2 indexed citations
6.
Khwa, Win-San, Jui-Jen Wu, Chuan-Jia Jhang, et al.. (2024). A 22nm Nonvolatile AI-Edge Processor with 21.4TFLOPS/W using 47.25Mb Lossless-Compressed-Computing STT-MRAM Near-Memory-Compute Macro. 1–2. 3 indexed citations
7.
Wen, Tai-Hao, Je-Min Hung, Wei-Hsing Huang, et al.. (2024). Fusion of memristor and digital compute-in-memory processing for energy-efficient edge computing. Science. 384(6693). 325–332. 47 indexed citations
8.
Wu, Ping-Chun, Win-San Khwa, Jui-Jen Wu, et al.. (2024). An Integer-Floating-Point Dual-Mode Gain-Cell Computing-in-Memory Macro for Advanced AI Edge Chips. IEEE Journal of Solid-State Circuits. 60(1). 158–170. 1 indexed citations
9.
Hsu, Tzu-Hsiang, Guan-Cheng Chen, Yiren Chen, et al.. (2023). A 0.8 V Intelligent Vision Sensor With Tiny Convolutional Neural Network and Programmable Weights Using Mixed-Mode Processing-in-Sensor Technique for Image Classification. IEEE Journal of Solid-State Circuits. 58(11). 3266–3274. 19 indexed citations
10.
Chen, Guan-Cheng, Tzu-Hsiang Hsu, Ren-Shuo Liu, et al.. (2023). A Multimode Vision Sensor With Temporal Contrast Pixel and Column-Parallel Local Binary Pattern Extraction for Dynamic Depth Sensing Using Stereo Vision. IEEE Journal of Solid-State Circuits. 58(10). 2767–2777. 4 indexed citations
11.
Wu, Ping-Chun, Jian-Wei Su, Yen-Lin Chung, et al.. (2022). A 28nm 1Mb Time-Domain Computing-in-Memory 6T-SRAM Macro with a 6.6ns Latency, 1241GOPS and 37.01TOPS/W for 8b-MAC Operations for Edge-AI Devices. 2022 IEEE International Solid- State Circuits Conference (ISSCC). 1–3. 91 indexed citations
12.
Xue, Cheng-Xin, Je-Min Hung, Hui-Yao Kao, et al.. (2021). 16.1 A 22nm 4Mb 8b-Precision ReRAM Computing-in-Memory Macro with 11.91 to 195.7TOPS/W for Tiny AI Edge Devices. 245–247. 143 indexed citations
13.
Hsu, Tzu-Hsiang, Yen-Kai Chen, Guan-Cheng Chen, et al.. (2021). A 0.8 V Multimode Vision Sensor for Motion and Saliency Detection With Ping-Pong PWM Pixel. IEEE Journal of Solid-State Circuits. 56(8). 2516–2524. 18 indexed citations
14.
Parlevliet, Patricia, А. В. Канаев, Chou P. Hung, et al.. (2021). Autonomous Flying With Neuromorphic Sensing. Frontiers in Neuroscience. 15. 672161–672161. 5 indexed citations
15.
Chang, Hui-Yun, et al.. (2021). Coordination through Inhibition: Control of Stabilizing and Updating Circuits in Spatial Orientation Working Memory. eNeuro. 8(5). ENEURO.0537–20.2021. 6 indexed citations
16.
Xue, Cheng-Xin, Ting-Wei Chang, Hui-Yao Kao, et al.. (2020). 15.4 A 22nm 2Mb ReRAM Compute-in-Memory Macro with 121-28TOPS/W for Multibit MAC Computing for Tiny AI Edge Devices. 244–246. 180 indexed citations
17.
Hsu, Tzu-Hsiang, Yiren Chen, Ren-Shuo Liu, et al.. (2020). A 0.5-V Real-Time Computational CMOS Image Sensor With Programmable Kernel for Feature Extraction. IEEE Journal of Solid-State Circuits. 56(5). 1588–1596. 65 indexed citations
18.
Shih, C. T., Olaf Sporns, Shouli Yuan, et al.. (2015). Connectomics-Based Analysis of Information Flow in the Drosophila Brain. Current Biology. 25(10). 1249–1258. 131 indexed citations
19.
Huang, Ying‐Zu, Chin‐Song Lu, John C. Rothwell, et al.. (2012). Modulation of the Disturbed Motor Network in Dystonia by Multisession Suppression of Premotor Cortex. PLoS ONE. 7(10). e47574–e47574. 41 indexed citations
20.
Lo, Chung‐Chuan, et al.. (1998). Effect of D57N Mutation on Membrane Activity and Molecular Unfolding of Cobra Cardiotoxin. Biophysical Journal. 75(5). 2382–2388. 9 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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