Runchun Wang

509 total citations
28 papers, 299 citations indexed

About

Runchun Wang is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience and Cellular and Molecular Neuroscience. According to data from OpenAlex, Runchun Wang has authored 28 papers receiving a total of 299 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Electrical and Electronic Engineering, 16 papers in Cognitive Neuroscience and 10 papers in Cellular and Molecular Neuroscience. Recurrent topics in Runchun Wang's work include Advanced Memory and Neural Computing (20 papers), Neural dynamics and brain function (11 papers) and Neuroscience and Neural Engineering (10 papers). Runchun Wang is often cited by papers focused on Advanced Memory and Neural Computing (20 papers), Neural dynamics and brain function (11 papers) and Neuroscience and Neural Engineering (10 papers). Runchun Wang collaborates with scholars based in Australia, India and United States. Runchun Wang's co-authors include André van Schaik, Tara Julia Hamilton, Jonathan Tapson, Chetan Singh Thakur, Gregory Cohen, Klaus M. Stiefel, Ying Xu, Alistair McEwan, Craig Jin and Ralph Etienne‐Cummings and has published in prestigious journals such as Frontiers in Neuroscience, Applied Sciences and IEEE Transactions on Circuits and Systems I Regular Papers.

In The Last Decade

Runchun Wang

26 papers receiving 291 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Runchun Wang Australia 10 256 160 111 78 20 28 299
Kristofor D. Carlson United States 8 202 0.8× 193 1.2× 89 0.8× 122 1.6× 11 0.6× 15 339
Kshitij Dhoble New Zealand 4 232 0.9× 200 1.3× 45 0.4× 126 1.6× 10 0.5× 4 306
Stephen Nease United States 9 329 1.3× 109 0.7× 94 0.8× 105 1.3× 9 0.5× 15 369
Nuttapod Nuntalid Switzerland 2 224 0.9× 193 1.2× 44 0.4× 120 1.5× 9 0.5× 2 296
Mehdi Aghagolzadeh United States 9 81 0.3× 159 1.0× 121 1.1× 65 0.8× 16 0.8× 22 263
Daniel Ben Dayan Rubin United States 8 359 1.4× 232 1.4× 141 1.3× 142 1.8× 11 0.6× 9 434
Stanisław Woźniak Switzerland 9 265 1.0× 102 0.6× 95 0.9× 120 1.5× 9 0.5× 20 334
X. Arreguit Switzerland 8 272 1.1× 93 0.6× 88 0.8× 50 0.6× 19 0.9× 17 321
Vishwa Goudar United States 9 91 0.4× 194 1.2× 64 0.6× 52 0.7× 10 0.5× 22 336
J. Lazzaro United States 8 140 0.5× 94 0.6× 54 0.5× 47 0.6× 39 1.9× 12 221

Countries citing papers authored by Runchun Wang

Since Specialization
Citations

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

Fields of papers citing papers by Runchun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Runchun Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Runchun Wang. A scholar is included among the top collaborators of Runchun Wang 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 Runchun Wang. Runchun Wang 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.
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Schaik, André van, et al.. (2024). Memory-efficient neurons and synapses for spike-timing-dependent-plasticity in large-scale spiking networks. Frontiers in Neuroscience. 18. 1450640–1450640.
4.
Xu, Ying, Saeed Afshar, Runchun Wang, et al.. (2021). A Biologically Inspired Sound Localisation System Using a Silicon Cochlea Pair. Applied Sciences. 11(4). 1519–1519. 7 indexed citations
5.
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Xu, Ying, et al.. (2018). CAR-Lite: A Multi-Rate Cochlear Model on FPGA for Spike-Based Sound Encoding. IEEE Transactions on Circuits and Systems I Regular Papers. 66(5). 1805–1817. 5 indexed citations
7.
Wang, Runchun & André van Schaik. (2018). Breaking Liebig’s Law: An Advanced Multipurpose Neuromorphic Engine. Frontiers in Neuroscience. 12. 593–593. 9 indexed citations
8.
Thakur, Chetan Singh, Runchun Wang, Tara Julia Hamilton, et al.. (2017). An Analogue Neuromorphic Co-Processor That Utilizes Device Mismatch for Learning Applications. IEEE Transactions on Circuits and Systems I Regular Papers. 65(4). 1174–1184. 16 indexed citations
9.
Wang, Runchun, Chetan Singh Thakur, Gregory Cohen, et al.. (2017). Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition. IEEE Transactions on Biomedical Circuits and Systems. 11(3). 574–584. 36 indexed citations
10.
Xu, Ying, et al.. (2016). Electronic cochlea: CAR-FAC model on FPGA. 8 indexed citations
11.
Wang, Runchun, Gregory Cohen, Chetan Singh Thakur, Jonathan Tapson, & André van Schaik. (2016). An SRAM-based implementation of a convolutional neural network. 560–563. 2 indexed citations
12.
Thakur, Chetan Singh, Tara Julia Hamilton, Runchun Wang, Jonathan Tapson, & André van Schaik. (2015). A neuromorphic hardware framework based on population coding. 2012. 1–8. 14 indexed citations
13.
Xu, Ying, Chetan Singh Thakur, Tara Julia Hamilton, et al.. (2015). A reconfigurable mixed-signal implementation of a neuromorphic ADC. 1–4. 3 indexed citations
14.
Wang, Runchun, Tara Julia Hamilton, Jonathan Tapson, & André van Schaik. (2014). A compact reconfigurable mixed-signal implementation of synaptic plasticity in spiking neurons. 47. 862–865. 9 indexed citations
15.
Wang, Runchun, Tara Julia Hamilton, Jonathan Tapson, & André van Schaik. (2014). A compact neural core for digital implementation of the Neural Engineering Framework. 548–551. 7 indexed citations
16.
Wang, Runchun, Tara Julia Hamilton, Jonathan Tapson, & André van Schaik. (2014). A generalised conductance-based silicon neuron for large-scale spiking neural networks. 48. 1564–1567. 11 indexed citations
17.
Wang, Runchun, Gregory Cohen, Klaus M. Stiefel, et al.. (2013). An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation. Frontiers in Neuroscience. 7. 14–14. 56 indexed citations
18.
Wang, Runchun. (2013). Neuromorphic implementations of polychronous spiking neural networks. 1 indexed citations
19.
Wang, Runchun, Craig Jin, Alistair McEwan, & André van Schaik. (2011). A programmable axonal propagation delay circuit for time-delay spiking neural networks. 869–872. 14 indexed citations
20.
Wang, Runchun, Jonathan Tapson, Tara Julia Hamilton, & André van Schaik. (2011). An analogue VLSI implementation of polychromous spiking neural networks. 18. 97–102. 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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