Omri Barak

6.2k total citations · 2 hit papers
39 papers, 3.4k citations indexed

About

Omri Barak is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Omri Barak has authored 39 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Cognitive Neuroscience, 18 papers in Artificial Intelligence and 14 papers in Electrical and Electronic Engineering. Recurrent topics in Omri Barak's work include Neural dynamics and brain function (28 papers), Advanced Memory and Neural Computing (14 papers) and Neural Networks and Applications (13 papers). Omri Barak is often cited by papers focused on Neural dynamics and brain function (28 papers), Advanced Memory and Neural Computing (14 papers) and Neural Networks and Applications (13 papers). Omri Barak collaborates with scholars based in Israel, United States and Mexico. Omri Barak's co-authors include Misha Tsodyks, Gianluigi Mongillo, Mattia Rigotti, Stefano Fusi, David Sussillo, Xiao‐Jing Wang, Nathaniel D. Daw, Melissa R. Warden, Earl K. Miller and Ranulfo Romo and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Omri Barak

38 papers receiving 3.3k citations

Hit Papers

The importance of mixed selectivity in complex cognitive ... 2008 2026 2014 2020 2013 2008 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Omri Barak Israel 20 2.9k 1.1k 635 585 226 39 3.4k
Christian K. Machens Portugal 27 3.0k 1.0× 1.3k 1.2× 607 1.0× 486 0.8× 247 1.1× 50 3.5k
David Sussillo United States 23 3.6k 1.2× 1.3k 1.2× 905 1.4× 1.1k 1.9× 286 1.3× 34 4.4k
Tim P. Vogels United Kingdom 19 2.3k 0.8× 1.5k 1.4× 907 1.4× 345 0.6× 232 1.0× 35 2.9k
Sonja Grün Germany 29 2.8k 1.0× 1.5k 1.4× 412 0.6× 346 0.6× 204 0.9× 107 3.3k
Byron M. Yu United States 35 5.1k 1.8× 2.6k 2.4× 757 1.2× 549 0.9× 179 0.8× 83 5.5k
Dan F. M. Goodman France 19 2.0k 0.7× 1.2k 1.1× 1.1k 1.7× 415 0.7× 139 0.6× 43 2.6k
Máté Lengyel United Kingdom 26 2.4k 0.8× 822 0.7× 359 0.6× 442 0.8× 117 0.5× 75 3.0k
Nicole C. Rust United States 23 3.3k 1.1× 1.1k 1.0× 266 0.4× 319 0.5× 424 1.9× 43 4.0k
Jeffrey L. Krichmar United States 30 1.8k 0.6× 905 0.8× 982 1.5× 783 1.3× 223 1.0× 122 3.2k
Mattia Rigotti United States 15 2.3k 0.8× 826 0.7× 306 0.5× 415 0.7× 178 0.8× 24 2.8k

Countries citing papers authored by Omri Barak

Since Specialization
Citations

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

Fields of papers citing papers by Omri Barak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Omri Barak

This figure shows the co-authorship network connecting the top 25 collaborators of Omri Barak. A scholar is included among the top collaborators of Omri Barak 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 Omri Barak. Omri Barak 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.
Macke, Jakob H., et al.. (2024). Trained recurrent neural networks develop phase-locked limit cycles in a working memory task. PLoS Computational Biology. 20(2). e1011852–e1011852. 5 indexed citations
2.
Barak, Omri & Misha Tsodyks. (2023). Mathematical models of learning and what can be learned from them. Current Opinion in Neurobiology. 80. 102721–102721. 2 indexed citations
3.
Mastrogiuseppe, Francesca, et al.. (2021). Quality of internal representation shapes learning performance in feedback neural networks. Physical Review Research. 3(1). 11 indexed citations
4.
Brenner, Naama, et al.. (2020). Scale free topology as an effective feedback system. PLoS Computational Biology. 16(5). e1007825–e1007825. 5 indexed citations
5.
Benisty, Hadas, Brett D. Mensh, Yitzhak Schiller, et al.. (2020). Cell-Type-Specific Outcome Representation in the Primary Motor Cortex. Neuron. 107(5). 954–971.e9. 44 indexed citations
6.
Barak, Omri, et al.. (2020). Local and global features of genetic networks supporting a phenotypic switch. PLoS ONE. 15(9). e0238433–e0238433. 3 indexed citations
7.
Barak, Omri. (2017). Recurrent neural networks as versatile tools of neuroscience research. Current Opinion in Neurobiology. 46. 1–6. 126 indexed citations
8.
Barak, Omri, et al.. (2017). Local Dynamics in Trained Recurrent Neural Networks. Physical Review Letters. 118(25). 258101–258101. 37 indexed citations
9.
Furst, Miriam, et al.. (2017). A New Approach to Model Pitch Perception Using Sparse Coding. PLoS Computational Biology. 13(1). e1005338–e1005338. 4 indexed citations
10.
Xu, Tie & Omri Barak. (2017). Dynamical Timescale Explains Marginal Stability in Excitability Dynamics. Journal of Neuroscience. 37(17). 4508–4524. 4 indexed citations
11.
Barak, Omri, et al.. (2017). Grid Cells Encode Local Positional Information. Current Biology. 27(15). 2337–2343.e3. 44 indexed citations
12.
Zeevi‐Levin, Naama, Revital Schick, Ronen Ben Jehuda, et al.. (2016). Developmental changes in electrophysiological characteristics of human-induced pluripotent stem cell–derived cardiomyocytes. Heart Rhythm. 13(12). 2379–2387. 33 indexed citations
13.
Lafuente, Víctor de, et al.. (2015). Dynamic Control of Response Criterion in Premotor Cortex during Perceptual Detection under Temporal Uncertainty. Neuron. 86(4). 1067–1077. 81 indexed citations
14.
Barak, Omri, Mattia Rigotti, & Stefano Fusi. (2013). The Sparseness of Mixed Selectivity Neurons Controls the Generalization–Discrimination Trade-Off. Journal of Neuroscience. 33(9). 3844–3856. 128 indexed citations
15.
Barak, Omri, David Sussillo, Ranulfo Romo, Misha Tsodyks, & L. F. Abbott. (2013). From fixed points to chaos: Three models of delayed discrimination. Progress in Neurobiology. 103. 214–222. 103 indexed citations
16.
Rigotti, Mattia, Omri Barak, Melissa R. Warden, et al.. (2013). The importance of mixed selectivity in complex cognitive tasks. Nature. 497(7451). 585–590. 972 indexed citations breakdown →
17.
Barak, Omri, Misha Tsodyks, & Ranulfo Romo. (2010). Neuronal Population Coding of Parametric Working Memory. Journal of Neuroscience. 30(28). 9424–9430. 136 indexed citations
18.
Okun, Michael S., et al.. (2007). Stochastic Emergence of Repeating Cortical Motifs in Spontaneous Membrane Potential Fluctuations In Vivo. Neuron. 53(3). 413–425. 61 indexed citations
19.
Barak, Omri & Misha Tsodyks. (2007). Persistent Activity in Neural Networks with Dynamic Synapses. PLoS Computational Biology. 3(2). e35–e35. 86 indexed citations
20.
Szwed, Marcin, et al.. (2005). Responses of Trigeminal Ganglion Neurons to the Radial Distance of Contact During Active Vibrissal Touch. Journal of Neurophysiology. 95(2). 791–802. 79 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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