Ammar Belatreche
- Electrical and Electronic Engineering top 5%
- Cognitive Neuroscience top 2%
- Artificial Intelligence top 2%
- Management Science and Operations Research top 1%
- Cellular and Molecular Neuroscience top 5%
- Co-authors
- T.M. McGinnityLiam MaguireYuhua LiSonya ColemanAboozar TaherkhaniGeorgina CosmaBrendan GlackinHong Qu
- Topics
- Advanced Memory and Neural Computing (38 papers)Neural dynamics and brain function (31 papers)Stock Market Forecasting Methods (19 papers)
- Journals
- Expert Systems with ApplicationsIEEE AccessIEEE Transactions on Neural Networks and Learning Systems
- Partner nations
- United KingdomChinaUnited States
In The Last Decade
Ammar Belatreche
79 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 110
- Electrical and Electronic Engineering 1.1k
- Cognitive Neuroscience 800
- Artificial Intelligence 789
- Management Science and Operations Research 407
- Cellular and Molecular Neuroscience 326
Countries citing papers authored by Ammar Belatreche
This map shows the geographic impact of Ammar Belatreche'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 Ammar Belatreche with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ammar Belatreche more than expected).
Fields of papers citing papers by Ammar Belatreche
This network shows the impact of papers produced by Ammar Belatreche. 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 Ammar Belatreche. The network helps show where Ammar Belatreche may publish in the future.
Co-authorship network of co-authors of Ammar Belatreche
This figure shows the co-authorship network connecting the top 25 collaborators of Ammar Belatreche. A scholar is included among the top collaborators of Ammar Belatreche 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 Ammar Belatreche. Ammar Belatreche is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | Spike-Timing-Dependent Back Propagation in Deep Spiking Neural Networks | 15 |
| 5 | 13 | |
| 6 | A review of learning in biologically plausible spiking neural networksbreakdown → | 268 |
| 7 | 63 | |
| 8 | 5 | |
| 9 | 21 | |
| 10 | A new biologically plausible supervised learning method for spiking neurons | 9 |
| 11 | 1 | |
| 12 | 21 | |
| 13 | 32 | |
| 14 | 2 | |
| 15 | Biologically Inspired Neural Networks | 3 |
| 16 | 2 | |
| 17 | 12 | |
| 18 | Edge Detection Based on Spiking Neural Network Model | 15 |
| 19 | 16 | |
| 20 | 7 |
About Ammar Belatreche
Ammar Belatreche is a scholar working on Cognitive Neuroscience, Management Science and Operations Research and Artificial Intelligence, having authored 85 papers that have together received 2.0k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (38 papers), Neural dynamics and brain function (31 papers) and Stock Market Forecasting Methods (19 papers). The work is most often cited by research in Cognitive Neuroscience (800 citations), Management Science and Operations Research (407 citations) and Artificial Intelligence (789 citations). Ammar Belatreche has collaborated with scholars based in United Kingdom, China and United States. Frequent co-authors include T.M. McGinnity, Liam Maguire, Yuhua Li, Sonya Coleman, Aboozar Taherkhani, Georgina Cosma, Brendan Glackin, Hong Qu, J. Wang and Ahmed Bouridane. Their work appears in journals such as Expert Systems with Applications, IEEE Access and IEEE Transactions on Neural Networks and Learning Systems.
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.