Richárd Fiáth

1.8k total citations
44 papers, 969 citations indexed

About

Richárd Fiáth is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Electrical and Electronic Engineering. According to data from OpenAlex, Richárd Fiáth has authored 44 papers receiving a total of 969 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Cellular and Molecular Neuroscience, 37 papers in Cognitive Neuroscience and 16 papers in Electrical and Electronic Engineering. Recurrent topics in Richárd Fiáth's work include Neuroscience and Neural Engineering (33 papers), Neural dynamics and brain function (29 papers) and EEG and Brain-Computer Interfaces (16 papers). Richárd Fiáth is often cited by papers focused on Neuroscience and Neural Engineering (33 papers), Neural dynamics and brain function (29 papers) and EEG and Brain-Computer Interfaces (16 papers). Richárd Fiáth collaborates with scholars based in Hungary, Germany and Belgium. Richárd Fiáth's co-authors include István Ulbert, Gergely Márton, Kinga Tóth, Patrick Ruther, Ferenc Mátyás, Chris Van Hoof, Alexandru Andrei, Silke Musa, Carolina Mora López and Bogdan Raducanu and has published in prestigious journals such as Nature Communications, PLoS ONE and NeuroImage.

In The Last Decade

Richárd Fiáth

42 papers receiving 959 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Richárd Fiáth Hungary 17 752 554 315 155 75 44 969
Alexey Pimashkin Russia 13 518 0.7× 295 0.5× 298 0.9× 138 0.9× 99 1.3× 43 768
Andrea Slézia Hungary 17 630 0.8× 455 0.8× 79 0.3× 129 0.8× 80 1.1× 22 856
Felicity Gore United States 9 543 0.7× 430 0.8× 135 0.4× 164 1.1× 136 1.8× 11 891
Chi Ren United States 12 385 0.5× 335 0.6× 140 0.4× 69 0.4× 45 0.6× 24 678
Alessandro Vato Italy 13 840 1.1× 698 1.3× 271 0.9× 147 0.9× 85 1.1× 29 1.0k
Gil S. Rind Australia 14 443 0.6× 307 0.6× 88 0.3× 118 0.8× 142 1.9× 28 661
M. M. Goldin Russia 12 662 0.9× 517 0.9× 126 0.4× 65 0.4× 227 3.0× 46 1.1k
Vanessa Tolosa United States 13 760 1.0× 601 1.1× 361 1.1× 207 1.3× 73 1.0× 27 1.1k
Marie Engelene J. Obien Japan 12 832 1.1× 452 0.8× 430 1.4× 301 1.9× 86 1.1× 27 1.1k
Evgueniy V. Lubenov United States 11 1.3k 1.8× 1.6k 2.9× 215 0.7× 80 0.5× 76 1.0× 12 1.8k

Countries citing papers authored by Richárd Fiáth

Since Specialization
Citations

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

Fields of papers citing papers by Richárd Fiáth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Richárd Fiáth

This figure shows the co-authorship network connecting the top 25 collaborators of Richárd Fiáth. A scholar is included among the top collaborators of Richárd Fiáth 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 Richárd Fiáth. Richárd Fiáth 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.
Fiáth, Richárd, et al.. (2024). High density laminar recordings reveal cell type and layer specific responses to infrared neural stimulation in the rat neocortex. Scientific Reports. 14(1). 31523–31523. 2 indexed citations
2.
3.
File, Bálint, Richárd Fiáth, Bea Pászthy, et al.. (2023). Adolescent ADHD and electrophysiological reward responsiveness: A machine learning approach to evaluate classification accuracy and prognosis. Psychiatry Research. 323. 115139–115139. 16 indexed citations
4.
Király, B., Andor Domonkos, Márta Jelitai, et al.. (2023). The medial septum controls hippocampal supra-theta oscillations. Nature Communications. 14(1). 6159–6159. 15 indexed citations
5.
Fiáth, Richárd, et al.. (2023). Thermal neuromodulation using pulsed and continuous infrared illumination in a penicillin-induced acute epilepsy model. Scientific Reports. 13(1). 14460–14460. 3 indexed citations
6.
Pászthy, Bea, et al.. (2022). Reliability of reward ERPs in middle‐late adolescents using a custom and a standardized preprocessing pipeline. Psychophysiology. 59(8). e14043–e14043. 6 indexed citations
7.
Fiáth, Richárd, et al.. (2022). From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings. Frontiers in Neuroinformatics. 16. 851024–851024. 10 indexed citations
8.
Martínez‐Bellver, Sergio, Richárd Fiáth, Andor Domonkos, et al.. (2022). Huygens synchronization of medial septal pacemaker neurons generates hippocampal theta oscillation. Cell Reports. 40(5). 111149–111149. 13 indexed citations
9.
Fiáth, Richárd, et al.. (2021). ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution. Journal of Neural Engineering. 18(4). 46033–46033. 8 indexed citations
10.
Ulbert, István, et al.. (2021). Dataset of cortical activity recorded with high spatial resolution from anesthetized rats. Scientific Data. 8(1). 180–180. 10 indexed citations
11.
Martínez‐Bellver, Sergio, Richárd Fiáth, Andor Domonkos, et al.. (2021). Huygens Synchronization of Medial Septal Pacemaker Neurons Generates Hippocampal Theta Oscillation. SSRN Electronic Journal. 2 indexed citations
12.
Fiáth, Richárd, et al.. (2021). A multimodal, implantable sensor array and measurement system to investigate the suppression of focal epileptic seizure using hypothermia. Journal of Neural Engineering. 18(4). 0460c3–0460c3. 6 indexed citations
13.
14.
Márton, Gergely, Lúcia Wittner, Richárd Fiáth, et al.. (2020). The neural tissue around SU-8 implants: A quantitative in vivo biocompatibility study. Materials Science and Engineering C. 112. 110870–110870. 31 indexed citations
15.
Fiáth, Richárd, et al.. (2019). Spike detection and sorting with deep learning. Journal of Neural Engineering. 17(1). 16038–16038. 52 indexed citations
16.
Fiáth, Richárd, et al.. (2019). A silicon-based spiky probe providing improved cell accessibility during in vitro slice recordings. Sensors and Actuators B Chemical. 297. 126649–126649. 2 indexed citations
17.
Fiáth, Richárd, Bogdan Raducanu, Silke Musa, et al.. (2018). Fine-scale mapping of cortical laminar activity during sleep slow oscillations using high-density linear silicon probes. Journal of Neuroscience Methods. 316. 58–70. 13 indexed citations
18.
Fiáth, Richárd, Bogdan Raducanu, Silke Musa, et al.. (2018). A silicon-based neural probe with densely-packed low-impedance titanium nitride microelectrodes for ultrahigh-resolution in vivo recordings. Biosensors and Bioelectronics. 106. 86–92. 54 indexed citations
19.
Fiáth, Richárd, Tibor Nánási, Kinga Tóth, et al.. (2018). Long-term recording performance and biocompatibility of chronically implanted cylindrically-shaped, polymer-based neural interfaces. Biomedizinische Technik/Biomedical Engineering. 63(3). 301–315. 20 indexed citations
20.
Gentet, Luc J., Richárd Fiáth, Michael Schwaerzle, et al.. (2017). Hybrid intracerebral probe with integrated bare LED chips for optogenetic studies. Biomedical Microdevices. 19(3). 49–49. 31 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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