Gábor Jandó

3.4k total citations · 2 hit papers
28 papers, 2.6k citations indexed

About

Gábor Jandó is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Epidemiology. According to data from OpenAlex, Gábor Jandó has authored 28 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Cognitive Neuroscience, 10 papers in Cellular and Molecular Neuroscience and 4 papers in Epidemiology. Recurrent topics in Gábor Jandó's work include Visual perception and processing mechanisms (12 papers), Neural dynamics and brain function (10 papers) and Neuroscience and Neuropharmacology Research (7 papers). Gábor Jandó is often cited by papers focused on Visual perception and processing mechanisms (12 papers), Neural dynamics and brain function (10 papers) and Neuroscience and Neuropharmacology Research (7 papers). Gábor Jandó collaborates with scholars based in Hungary, United States and Switzerland. Gábor Jandó's co-authors include György Buzsáki, Zoltán Nádasdy, Anatol Bragin, K.D. Wise, J.F. Hetke, Aarne Ylinen, Attila Sı́k, Imre Szabó, Melissa Van Landeghem and Zsolt Horváth and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and SHILAP Revista de lepidopterología.

In The Last Decade

Gábor Jandó

26 papers receiving 2.6k citations

Hit Papers

Gamma (40-100 Hz) oscillation in the hippocampus of the b... 1995 2026 2005 2015 1995 1995 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gábor Jandó Hungary 13 2.2k 2.0k 273 156 143 28 2.6k
Clayton T. Dickson Canada 32 2.5k 1.2× 2.2k 1.1× 437 1.6× 141 0.9× 167 1.2× 82 3.5k
Robert W. Komorowski United States 12 2.2k 1.0× 1.4k 0.7× 122 0.4× 93 0.6× 167 1.2× 17 2.5k
Markus Butz Germany 29 1.5k 0.7× 1.2k 0.6× 133 0.5× 172 1.1× 361 2.5× 78 2.8k
Ana D. de Lima Germany 22 1.0k 0.5× 1.4k 0.7× 580 2.1× 80 0.5× 109 0.8× 43 2.0k
Sean M. Montgomery United States 9 2.2k 1.0× 1.9k 0.9× 118 0.4× 54 0.3× 114 0.8× 13 2.5k
Andrea Bibbig United Kingdom 22 2.3k 1.1× 2.1k 1.1× 612 2.2× 179 1.1× 104 0.7× 23 2.9k
James A. Mazer United States 19 2.1k 1.0× 1.0k 0.5× 476 1.7× 170 1.1× 146 1.0× 25 2.7k
Daniel Ulrich United States 21 1.4k 0.6× 1.3k 0.7× 400 1.5× 73 0.5× 87 0.6× 36 1.9k
Richard Gao United States 10 1.8k 0.8× 769 0.4× 428 1.6× 120 0.8× 113 0.8× 18 2.6k
Ralf A. W. Galuske Germany 19 1.6k 0.7× 617 0.3× 237 0.9× 89 0.6× 85 0.6× 34 2.3k

Countries citing papers authored by Gábor Jandó

Since Specialization
Citations

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

Fields of papers citing papers by Gábor Jandó

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gábor Jandó

This figure shows the co-authorship network connecting the top 25 collaborators of Gábor Jandó. A scholar is included among the top collaborators of Gábor Jandó 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 Gábor Jandó. Gábor Jandó 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
2.
Hegyi, Péter, et al.. (2024). Mobileszköz-alapú gyermekkori látásszűrés a tompalátás korai felismerésére. Orvosi Hetilap. 165(16). 620–628.
3.
Thompson, Dorothy, et al.. (2023). ISCEV standard pattern reversal VEP development: paediatric reference limits from 649 healthy subjects. Documenta Ophthalmologica. 147(3). 147–164. 3 indexed citations
4.
Frigyik, Béla A., et al.. (2023). Artificial intelligence-based screening for amblyopia and its risk factors: comparison with four classic stereovision tests. Frontiers in Medicine. 10. 1294559–1294559. 3 indexed citations
5.
Horváth, Gábor, et al.. (2018). Simple reaction times to cyclopean stimuli reveal that the binocular system is tuned to react faster to near than to far objects. PLoS ONE. 13(1). e0188895–e0188895. 3 indexed citations
6.
Czigler, András, et al.. (2018). Validation of dynamic random dot stereotests in pediatric vision screening. Graefe s Archive for Clinical and Experimental Ophthalmology. 257(2). 413–423. 15 indexed citations
7.
Kiss, Huba, et al.. (2012). Effects of Luminance on Dynamic Random-Dot Correlogram Evoked Visual Potentials. Perception. 41(6). 648–660. 6 indexed citations
8.
Jandó, Gábor, et al.. (2012). Early-onset binocularity in preterm infants reveals experience-dependent visual development in humans. Proceedings of the National Academy of Sciences. 109(27). 11049–11052. 58 indexed citations
9.
Marko, K. A., et al.. (2009). Contrast independence of dynamic random dot correlogram evoked VEP amplitude. Journal of Vision. 9(4). 8–8. 11 indexed citations
10.
Jandó, Gábor, Tiziano Agostini, Alessandra Galmonte, & Nicola Bruno. (2003). Measuring surface achromatic color: Toward a common measure for increments and decrements. Behavior Research Methods, Instruments, & Computers. 35(1). 70–81. 6 indexed citations
11.
Sümegi, Balázs, et al.. (1997). Learning disturbances in offsprings of zidovudine (AZT) treated rats.. PubMed. 5(1). 83–5. 12 indexed citations
12.
Vadász, C., et al.. (1995). Genetic threshold hypothesis of neocortical spike‐and‐wave discharges in the rat: An animal model of petit mal epilepsy. American Journal of Medical Genetics. 60(1). 55–63. 18 indexed citations
13.
Jandó, Gábor, et al.. (1995). Spike-and-wave epilepsy in rats: Sex differences and inheritance of physiological traits. Neuroscience. 64(2). 301–317. 58 indexed citations
14.
Jandó, Gábor, Ralph M. Siegel, Zsolt Horváth, & György Buzsáki. (1993). Pattern recognition of the electroencephalogram by artificial neural networks. Electroencephalography and Clinical Neurophysiology. 86(2). 100–109. 73 indexed citations
15.
Karádi, Zoltán, et al.. (1993). Computer analysis of single neuron activity during conditioned feeding task.. PubMed. 1(2). 147–55. 3 indexed citations
16.
Hajnal, A., Paul Sandor, Gábor Jandó, et al.. (1992). Feeding disturbances and EEG activity changes after amygdaloid kainate lesions in the rat. Brain Research Bulletin. 29(6). 909–916. 22 indexed citations
17.
Sándor, Péter, A. Hajnal, Gábor Jandó, Zoltán Karádi, & László Lénárd. (1992). Microelectrophoretic application of kainic acid into the globus pallidus: Disturbances in feeding behavior. Brain Research Bulletin. 28(5). 751–756. 14 indexed citations
18.
Lénárd, László, Paul Sandor, A. Hajnal, et al.. (1991). Sex-dependent body weight changes after iontophoretic application of kainic acid into the LH or VMH. Brain Research Bulletin. 26(1). 141–148. 14 indexed citations
19.
Lénárd, László, Zoltán Karádi, Gábor Jandó, et al.. (1991). Feeding and body weight regulation after 6-OHDA application into the preoptic area. Brain Research Bulletin. 27(3-4). 359–365. 10 indexed citations
20.
Lénárd, László, Gábor Jandó, Zoltán Karádi, A. Hajnal, & Péter Sándor. (1988). Lateral hypothalamic feeding mechanisms: Iontophoretic effects of kainic acid, ibotenic acid and 6-hydroxydopamine. Brain Research Bulletin. 20(6). 847–856. 23 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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