Joseph T. Lizier

6.8k total citations
90 papers, 3.8k citations indexed

About

Joseph T. Lizier is a scholar working on Cognitive Neuroscience, Statistical and Nonlinear Physics and Artificial Intelligence. According to data from OpenAlex, Joseph T. Lizier has authored 90 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Cognitive Neuroscience, 23 papers in Statistical and Nonlinear Physics and 22 papers in Artificial Intelligence. Recurrent topics in Joseph T. Lizier's work include Neural dynamics and brain function (42 papers), Advanced Memory and Neural Computing (13 papers) and Neural Networks and Applications (13 papers). Joseph T. Lizier is often cited by papers focused on Neural dynamics and brain function (42 papers), Advanced Memory and Neural Computing (13 papers) and Neural Networks and Applications (13 papers). Joseph T. Lizier collaborates with scholars based in Australia, Germany and United States. Joseph T. Lizier's co-authors include Mikhail Prokopenko, Michael Wibral, Albert Y. Zomaya, Viola Priesemann, Raúl Vicente, Michael Harré, Lionel Barnett, Terry Bossomaier, X. Rosalind Wang and Oliver Obst and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Physical Review Letters.

In The Last Decade

Joseph T. Lizier

86 papers receiving 3.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Joseph T. Lizier Australia 35 1.8k 924 678 488 422 90 3.8k
Zoltán Toroczkai United States 35 1.4k 0.8× 1.9k 2.0× 432 0.6× 342 0.7× 279 0.7× 96 5.7k
L. de Arcangelis Italy 36 902 0.5× 1.0k 1.1× 476 0.7× 245 0.5× 595 1.4× 136 4.3k
Alexander Kraskov United Kingdom 30 3.5k 2.0× 528 0.6× 999 1.5× 319 0.7× 420 1.0× 45 6.6k
Thomas Kreuz Italy 23 2.5k 1.4× 685 0.7× 253 0.4× 193 0.4× 313 0.7× 48 3.2k
Miguel A. Muñoz Spain 39 1.2k 0.7× 2.3k 2.5× 294 0.4× 289 0.6× 506 1.2× 160 5.5k
Ichiro Tsuda Japan 24 1.5k 0.8× 1.1k 1.2× 775 1.1× 178 0.4× 167 0.4× 91 2.7k
Eric D. Kolaczyk United States 34 666 0.4× 898 1.0× 1.1k 1.6× 173 0.4× 265 0.6× 115 4.8k
John M. Beggs United States 27 4.1k 2.2× 1.4k 1.5× 578 0.9× 732 1.5× 280 0.7× 66 5.0k
Viola Priesemann Germany 25 1.9k 1.1× 402 0.4× 288 0.4× 234 0.5× 329 0.8× 70 3.2k
Steven B. Lowen United States 31 1.2k 0.7× 744 0.8× 213 0.3× 202 0.4× 508 1.2× 68 3.1k

Countries citing papers authored by Joseph T. Lizier

Since Specialization
Citations

This map shows the geographic impact of Joseph T. Lizier'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 Joseph T. Lizier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joseph T. Lizier more than expected).

Fields of papers citing papers by Joseph T. Lizier

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joseph T. Lizier. 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 Joseph T. Lizier. The network helps show where Joseph T. Lizier may publish in the future.

Co-authorship network of co-authors of Joseph T. Lizier

This figure shows the co-authorship network connecting the top 25 collaborators of Joseph T. Lizier. A scholar is included among the top collaborators of Joseph T. Lizier 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 Joseph T. Lizier. Joseph T. Lizier 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.
Gohil, Chetan, Oliver M. Cliff, James M. Shine, Ben Fulcher, & Joseph T. Lizier. (2025). Cross Mutual Information. 815–820. 1 indexed citations
2.
Munn, Brandon, Eli J. Müller, Itia A. Favre‐Bulle, et al.. (2024). Multiscale organization of neuronal activity unifies scale-dependent theories of brain function. Cell. 187(25). 7303–7313.e15. 9 indexed citations
3.
Prokopenko, Mikhail, Paul Davies, Michael Harré, et al.. (2024). Biological arrow of time: emergence of tangled information hierarchies and self-modelling dynamics. Journal of Physics Complexity. 6(1). 15006–15006. 1 indexed citations
4.
Munn, Brandon, Eli J. Müller, Vicente Medel, et al.. (2023). Neuronal connected burst cascades bridge macroscale adaptive signatures across arousal states. Nature Communications. 14(1). 6846–6846. 13 indexed citations
5.
Burns, Alicia, T. M. Schaerf, Joseph T. Lizier, et al.. (2022). Self-organization and information transfer in Antarctic krill swarms. Proceedings of the Royal Society B Biological Sciences. 289(1969). 20212361–20212361. 11 indexed citations
6.
Hansen, Matthew J., Alicia Burns, Christopher T. Monk, et al.. (2021). The effect of predation risk on group behaviour and information flow during repeated collective decisions. Animal Behaviour. 173. 215–239. 8 indexed citations
7.
Lukeman, Ryan, et al.. (2019). Speed-mediated properties of schooling. Royal Society Open Science. 6(2). 181482–181482. 30 indexed citations
8.
Novelli, Leonardo, Patricia Wollstadt, Pedro A. M. Mediano, Michael Wibral, & Joseph T. Lizier. (2019). Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing. Network Neuroscience. 3(3). 827–847. 74 indexed citations
9.
Ward, Ashley J. W., T. M. Schaerf, Alicia Burns, et al.. (2018). Cohesion, order and information flow in the collective motion of mixed-species shoals. Royal Society Open Science. 5(12). 181132–181132. 35 indexed citations
10.
Lizier, Joseph T., et al.. (2017). Network analysis of task-oriented neuroimaging data via multivariate information-theoretic measures. BMC Neuroscience.
11.
Cliff, Oliver M., Joseph T. Lizier, X. Rosalind Wang, et al.. (2017). Quantifying Long-Range Interactions and Coherent Structure in Multi-Agent Dynamics. Artificial Life. 23(1). 34–57. 14 indexed citations
12.
Wollstadt, Patricia, et al.. (2017). Information-Theoretic Evidence for Predictive Coding in the Face-Processing System. Journal of Neuroscience. 37(34). 8273–8283. 29 indexed citations
13.
Wibral, Michael, William A. Phillips, Joseph T. Lizier, & Viola Priesemann. (2015). Partial information decomposition as a unified approach to the characterization and design of neural goal functions. BMC Neuroscience. 16(S1). 3 indexed citations
14.
Wibral, Michael, Joseph T. Lizier, & Viola Priesemann. (2014). How to measure local active information storage in neural systems. Max Planck Institute for Plasma Physics. 131–132. 2 indexed citations
15.
Wibral, Michael, Viola Priesemann, Felix Siebenhühner, et al.. (2013). Measuring Information-Transfer Delays. PLoS ONE. 8(2). e55809–e55809. 192 indexed citations
16.
Bauer, Frank & Joseph T. Lizier. (2012). Identifying influential spreaders. arXiv (Cornell University). 1 indexed citations
17.
Wang, X. Rosalind, et al.. (2012). Quantifying and Tracing Information Cascades in Swarms. PLoS ONE. 7(7). e40084–e40084. 65 indexed citations
18.
Wang, X. Rosalind, Joseph T. Lizier, & Mikhail Prokopenko. (2010). A Fisher Information Study of Phase Transitions in Random Boolean Networks. Artificial Life. 305–312. 4 indexed citations
19.
Lizier, Joseph T., Mikhail Prokopenko, Ivan Tanev, & Albert Y. Zomaya. (2008). Emergence of Glider-like Structures in a Modular Robotic System. Artificial Life. 366–373. 8 indexed citations
20.
Lizier, Joseph T. & Graham Town. (2001). Splice losses in holey optical fibers. IEEE Photonics Technology Letters. 13(8). 794–796. 49 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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