Milad Mozafari
- Cognitive Neuroscience top 5%
- Neural dynamics and brain function 9
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- Advanced Memory and Neural Computing 7
- CCD and CMOS Imaging Sensors 2
- Ferroelectric and Negative Capacitance Devices 2
- Artificial Intelligence top 10%
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- Visual Attention and Saliency Detection 1
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- Cell Image Analysis Techniques 4
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- Optimization and Search Problems 2
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- Cellular Automata and Applications 2
- Co-authors
- Mohammad GanjtabeshTimothée MasquelierAbbas Nowzari-DaliniSimon J. ThorpeSaeed Reza KheradpishehRufin VanRullenLeila ReddyHamid Beigy
- Cited by
- Cognitive NeuroscienceElectrical and Electronic EngineeringCellular and Molecular Neuroscience
In The Last Decade
Milad Mozafari
11 papers receiving 418 citations
Peers
Comparison fields: 5 of 47
- Cognitive Neuroscience 271
- Electrical and Electronic Engineering 328
- Cellular and Molecular Neuroscience 94
- Artificial Intelligence 133
- Computer Vision and Pattern Recognition 42
Countries citing papers authored by Milad Mozafari
This map shows the geographic impact of Milad Mozafari'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 Milad Mozafari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Milad Mozafari more than expected).
Fields of papers citing papers by Milad Mozafari
This network shows the impact of papers produced by Milad Mozafari. 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 Milad Mozafari. The network helps show where Milad Mozafari may publish in the future.
Co-authorship network
The 10 scholars most cited alongside Milad Mozafari, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 4 | |
| 2 | 2022 | 0 | |
| 3 | 2022 | 30 | |
| 4 | 2021 | 11 | |
| 5 | Brain-inspired predictive coding dynamics improve the robustness of deep neural networks | 2020 | 1 |
| 6 | 2019 | 78 | |
| 7 | 2019 | 119 | |
| 8 | Bio-Inspired Digit Recognition Using Spike-Timing-Dependent Plasticity (STDP) and Reward-Modulated STDP in Deep Convolutional Networks | 2018 | 3 |
| 9 | Combining STDP and Reward-Modulated STDP in Deep Convolutional Spiking Neural Networks for Digit Recognition | 2018 | 24 |
| 10 | 2018 | 139 | |
| 11 | 2015 | 10 | |
| 12 | 2013 | 11 |
About Milad Mozafari
Milad Mozafari is a scholar working on Biophysics, Cognitive Neuroscience and Sensory Systems, having authored 12 papers that have together received 430 indexed citations. Recurring topics across this work include Neural dynamics and brain function (9 papers), Advanced Memory and Neural Computing (7 papers), Cell Image Analysis Techniques (4 papers), CCD and CMOS Imaging Sensors (2 papers), Optimization and Search Problems (2 papers), Ferroelectric and Negative Capacitance Devices (2 papers), Cellular Automata and Applications (2 papers) and Visual Attention and Saliency Detection (1 paper). The work is most often cited by research in Cognitive Neuroscience (271 citations), Electrical and Electronic Engineering (328 citations) and Cellular and Molecular Neuroscience (94 citations). Milad Mozafari has collaborated with scholars based in France, Iran and Morocco. Frequent co-authors include Mohammad Ganjtabesh, Timothée Masquelier, Abbas Nowzari-Dalini, Simon J. Thorpe, Saeed Reza Kheradpisheh, Rufin VanRullen, Leila Reddy, Hamid Beigy, Mohammad Ebrahim Shiri and Andrea Alamia. Their work appears in journals such as Pattern Recognition, IEEE Transactions on Neural Networks and Learning Systems and Neurocomputing.
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.