Daniel Chicharro

1.4k total citations
23 papers, 823 citations indexed

About

Daniel Chicharro is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Statistical and Nonlinear Physics. According to data from OpenAlex, Daniel Chicharro has authored 23 papers receiving a total of 823 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Cognitive Neuroscience, 8 papers in Cellular and Molecular Neuroscience and 8 papers in Statistical and Nonlinear Physics. Recurrent topics in Daniel Chicharro's work include Neural dynamics and brain function (19 papers), Functional Brain Connectivity Studies (6 papers) and stochastic dynamics and bifurcation (6 papers). Daniel Chicharro is often cited by papers focused on Neural dynamics and brain function (19 papers), Functional Brain Connectivity Studies (6 papers) and stochastic dynamics and bifurcation (6 papers). Daniel Chicharro collaborates with scholars based in Italy, Spain and United States. Daniel Chicharro's co-authors include Ralph G. Andrzejak, Florian Mormann, Stefano Panzeri, Thomas Kreuz, Anders Ledberg, Christian E. Elger, Conor Houghton, Klaus Lehnertz, Martin Greschner and Stan L. Pashkovski and has published in prestigious journals such as Nature, Nature Communications and Journal of Neuroscience.

In The Last Decade

Daniel Chicharro

22 papers receiving 803 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Chicharro Italy 17 596 246 138 81 78 23 823
Oren Shriki Israel 18 1.0k 1.7× 242 1.0× 249 1.8× 111 1.4× 46 0.6× 46 1.2k
Brian C. Burke United States 10 616 1.0× 197 0.8× 140 1.0× 186 2.3× 56 0.7× 12 818
Vadim V. Nikouline Finland 10 1.4k 2.3× 191 0.8× 148 1.1× 61 0.8× 18 0.2× 15 1.6k
Pedro A. M. Mediano United Kingdom 20 769 1.3× 136 0.6× 209 1.5× 160 2.0× 30 0.4× 68 1.3k
Søren Rahn Christensen Denmark 16 908 1.5× 252 1.0× 95 0.7× 46 0.6× 39 0.5× 23 1.2k
E. Başar Türkiye 20 1.1k 1.9× 282 1.1× 96 0.7× 87 1.1× 41 0.5× 31 1.2k
R. H. Jindra Austria 7 453 0.8× 164 0.7× 64 0.5× 104 1.3× 61 0.8× 14 742
Bill Baird United States 10 399 0.7× 105 0.4× 87 0.6× 162 2.0× 82 1.1× 29 572
Matthäus Staniek Germany 9 535 0.9× 233 0.9× 235 1.7× 84 1.0× 9 0.1× 9 1.1k
Bernhard Hellwig Germany 17 733 1.2× 632 2.6× 76 0.6× 77 1.0× 7 0.1× 34 1.5k

Countries citing papers authored by Daniel Chicharro

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Chicharro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Chicharro

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Chicharro. A scholar is included among the top collaborators of Daniel Chicharro 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 Daniel Chicharro. Daniel Chicharro 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.
Chicharro, Daniel, Stefano Panzeri, & Ralf M. Haefner. (2021). Stimulus-dependent relationships between behavioral choice and sensory neural responses. eLife. 10. 5 indexed citations
2.
Pashkovski, Stan L., Giuliano Iurilli, David H. Brann, et al.. (2020). Structure and flexibility in cortical representations of odour space. Nature. 583(7815). 253–258. 91 indexed citations
3.
Chen, Xing, et al.. (2018). Perceptual learning of fine contrast discrimination changes neuronal tuning and population coding in macaque V4. Nature Communications. 9(1). 4238–4238. 17 indexed citations
4.
Pica, Giuseppe, Eugenio Piasini, Daniel Chicharro, & Stefano Panzeri. (2017). Invariant Components of Synergy, Redundancy, and Unique Information among Three Variables. Entropy. 19(9). 451–451. 19 indexed citations
5.
Thiele, Alexander, Christian Brandt, Miguel Dasilva, et al.. (2016). Attention Induced Gain Stabilization in Broad and Narrow-Spiking Cells in the Frontal Eye-Field of Macaque Monkeys. Journal of Neuroscience. 36(29). 7601–7612. 29 indexed citations
6.
Brovelli, Andrea, Daniel Chicharro, Jean‐Michel Badier, Huifang Wang, & Viktor Jirsa. (2015). Characterization of Cortical Networks and Corticocortical Functional Connectivity Mediating Arbitrary Visuomotor Mapping. Journal of Neuroscience. 35(37). 12643–12658. 40 indexed citations
7.
Chicharro, Daniel & Stefano Panzeri. (2014). Algorithms of causal inference for the analysis of effective connectivity among brain regions. Frontiers in Neuroinformatics. 8. 64–64. 25 indexed citations
8.
Chicharro, Daniel. (2014). A Causal Perspective on the Analysis of Signal and Noise Correlations and Their Role in Population Coding. Neural Computation. 26(6). 999–1054. 6 indexed citations
9.
Chicharro, Daniel & Anders Ledberg. (2012). When Two Become One: The Limits of Causality Analysis of Brain Dynamics. PLoS ONE. 7(3). e32466–e32466. 45 indexed citations
10.
Chicharro, Daniel & Anders Ledberg. (2012). Framework to study dynamic dependencies in networks of interacting processes. Physical Review E. 86(4). 41901–41901. 36 indexed citations
11.
Chicharro, Daniel, Thomas Kreuz, & Ralph G. Andrzejak. (2011). What can spike train distances tell us about the neural code?. Journal of Neuroscience Methods. 199(1). 146–165. 19 indexed citations
12.
Chicharro, Daniel. (2011). On the spectral formulation of Granger causality. Biological Cybernetics. 105(5-6). 331–347. 74 indexed citations
13.
Andrzejak, Ralph G., Daniel Chicharro, Klaus Lehnertz, & Florian Mormann. (2011). Using bivariate signal analysis to characterize the epileptic focus: The benefit of surrogates. Physical Review E. 83(4). 46203–46203. 46 indexed citations
14.
Chicharro, Daniel, Ralph G. Andrzejak, & Anders Ledberg. (2011). Inferring and quantifying causality in neuronal networks. BMC Neuroscience. 12(S1). 6 indexed citations
15.
Kreuz, Thomas, Daniel Chicharro, Martin Greschner, & Ralph G. Andrzejak. (2010). Time-resolved and time-scale adaptive measures of spike train synchrony. Journal of Neuroscience Methods. 195(1). 92–106. 49 indexed citations
16.
Chicharro, Daniel & Ralph G. Andrzejak. (2009). Reliable detection of directional couplings using rank statistics. Physical Review E. 80(2). 26217–26217. 89 indexed citations
17.
Kreuz, Thomas, Daniel Chicharro, & Ralph G. Andrzejak. (2009). Measuring spike train synchrony between neuronal populations. BMC Neuroscience. 10(S1). 2 indexed citations
18.
Andrzejak, Ralph G., Daniel Chicharro, Christian E. Elger, & Florian Mormann. (2009). Seizure prediction: Any better than chance?. Clinical Neurophysiology. 120(8). 1465–1478. 78 indexed citations
19.
Kreuz, Thomas, Daniel Chicharro, Ralph G. Andrzejak, Julie S. Haas, & Henry D. I. Abarbanel. (2009). Measuring multiple spike train synchrony. Journal of Neuroscience Methods. 183(2). 287–299. 35 indexed citations
20.
Kreuz, Thomas, Daniel Chicharro, Ralph G. Andrzejak, et al.. (2008). Measuring spike train reliability. BMC Neuroscience. 9(S1). 1 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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