Liam Paninski

1.5k citations
12 papers · 725 indexed · h-index 9

Impact in

Papers in

    • Neural dynamics and brain function 10
    • EEG and Brain-Computer Interfaces 2
    • Functional Brain Connectivity Studies 2
    • Motor Control and Adaptation 1
    • Neuroscience and Neural Engineering 3
    • Neuroscience and Neuropharmacology Research 2

Liam Paninski

12 papers receiving 700 citations

Peers

Liam Paninski
Comparison fields: 5 of 81
  • Cognitive Neuroscience 573
  • Cellular and Molecular Neuroscience 338
  • Biophysics 26
  • Artificial Intelligence 135
  • Developmental Biology 8
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Yashar Ahmadian United States
Guillaume Hennequin United Kingdom
Devika Narain Netherlands
Christian W. Eurich Germany
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Citations per field
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Citations per year

Countries citing papers authored by Liam Paninski

Since Specialization
Citations

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

Fields of papers citing papers by Liam Paninski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Liam Paninski, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Liam Paninski Line = papers co-authored together Liam Paninski links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1
A zero-inflated gamma model for post-deconvolved calcium imaging traces
20201
2
Clustered factor analysis of multineuronal spike data
201411
3 201164
4 20115
5 201134
6 200883
7 20061
8 2004289
9
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model
20039
10
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model
200330
11 200376
12 2003122

About Liam Paninski

Liam Paninski is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience, Biophysics, Artificial Intelligence and Statistics and Probability, having authored 12 papers that have together received 725 indexed citations. Recurring topics across this work include Neural dynamics and brain function (10 papers), Neural Networks and Applications (5 papers), Neuroscience and Neural Engineering (3 papers), EEG and Brain-Computer Interfaces (2 papers), Neuroscience and Neuropharmacology Research (2 papers), Advanced Memory and Neural Computing (2 papers), Functional Brain Connectivity Studies (2 papers) and Motor Control and Adaptation (1 paper). The work is most often cited by research in Cognitive Neuroscience (573 citations), Cellular and Molecular Neuroscience (338 citations), Biophysics (26 citations), Artificial Intelligence (135 citations) and Developmental Biology (8 citations). Liam Paninski has collaborated with scholars based in United States and Poland. Frequent co-authors include John P. Donoghue, Matthew Fellows, Nicholas G. Hatsopoulos, Joshua T Vogelstein, Yuriy Mishchenko, Nicholas G. Hatsopoulos, Mijail D. Serruya, Jonathan W. Pillow, Eero P. Simoncelli and Rafael Yuste. Their work appears in journals such as The Annals of Applied Statistics, Biological Cybernetics, Network Computation in Neural Systems, Journal of Neurophysiology and IEEE Transactions on Information Theory.

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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