Max Ward

1.0k citations
28 papers · 506 · 1 hit paper · h-index 10

Impact in

Papers in

Max Ward

24 papers receiving 482 citations

Max Ward's Hit Papers

Training Spiking Neural Networks Using Lessons From Deep Learning 2023 · 297 citations
2970+1+2Years since publication50100150200250

Peers

Max Ward
Comparison fields: 5 of 79
  • Cognitive Neuroscience 108
  • Electrical and Electronic Engineering 234
  • Artificial Intelligence 121
  • Cellular and Molecular Neuroscience 54
  • Computer Networks and Communications 39
Replace Hanlin Tang with:
Hanlin Tang United States
Frank Pasemann Germany
Toru Aonishi Japan
Yuwei Cui United States
F. Javier Toledo Spain
Eyal Hulata Israel
Hugo de Garis United States
Lou Scheffer United States
Thiago Mosqueiro United States
J.B. Butcher United Kingdom
Max Ward relative to Hanlin Tang United States Hanlin Tang's profile →
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Countries citing papers authored by Max Ward

Since Specialization
Citations

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

Fields of papers citing papers by Max Ward

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Max Ward, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Max Ward Line = papers co-authored together Max Ward links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Training Spiking Neural Networks Using Lessons From Deep Learning
Hit paper breakdown →
2023297
2 202240
3 201721
4 196021
5 201720
6 195414
7 202313
8 202211
9 20239
10 20239
11 19609
12 20237
13 20236
14 20235
15 19635
16 20195
17 20254
18 19643
19 20251
20 20251

About Max Ward

Max Ward is a scholar working on Molecular Biology, Computer Networks and Communications, Ecology, Evolution, Behavior and Systematics, Plant Science and Pulmonary and Respiratory Medicine, having authored 28 papers that have together received 506 indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (9 papers), RNA modifications and cancer (7 papers), RNA Research and Splicing (6 papers), Advanced Malware Detection Techniques (3 papers), Information and Cyber Security (3 papers), Network Security and Intrusion Detection (3 papers), Artificial Intelligence in Healthcare and Education (2 papers) and Hemodynamic Monitoring and Therapy (2 papers). The work is most often cited by research in Cognitive Neuroscience (108 citations), Electrical and Electronic Engineering (234 citations), Artificial Intelligence (121 citations), Cellular and Molecular Neuroscience (54 citations) and Computer Networks and Communications (39 citations). Max Ward has collaborated with scholars based in Australia, United States and Germany. Frequent co-authors include Girish Dwivedi, Jason K. Eshraghian, Emre Neftci, Gregor Lenz, Wei Lü, Doo Seok Jeong, Mohammed Bennamoun, Xinxin Wang, Amitava Datta and David H. Mathews. Their work appears in journals such as Bioinformatics, Nucleic Acids Research, Journal of Graph Algorithms and Applications, Information Sciences and Biomedical Optics Express.

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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