Luca Ambrogioni

860 total citations
24 papers, 387 citations indexed

About

Luca Ambrogioni is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Luca Ambrogioni has authored 24 papers receiving a total of 387 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Cognitive Neuroscience, 10 papers in Artificial Intelligence and 5 papers in Electrical and Electronic Engineering. Recurrent topics in Luca Ambrogioni's work include Neural dynamics and brain function (10 papers), Functional Brain Connectivity Studies (6 papers) and Advanced Memory and Neural Computing (5 papers). Luca Ambrogioni is often cited by papers focused on Neural dynamics and brain function (10 papers), Functional Brain Connectivity Studies (6 papers) and Advanced Memory and Neural Computing (5 papers). Luca Ambrogioni collaborates with scholars based in Netherlands, Germany and France. Luca Ambrogioni's co-authors include Marcel van Gerven, Eric Maris, Umut Güçlü, Yağmur Güçlütürk, Katja Seeliger, Alice Tomassini, W. Pieter Medendorp, Diego Vidaurre, Nadine Dijkstra and Max Hinne and has published in prestigious journals such as PLoS ONE, NeuroImage and Trends in Cognitive Sciences.

In The Last Decade

Luca Ambrogioni

21 papers receiving 382 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Luca Ambrogioni Netherlands 8 264 79 41 36 35 24 387
Ghislain St-Yves United States 8 326 1.2× 90 1.1× 46 1.1× 29 0.8× 35 1.0× 12 470
Katja Seeliger Netherlands 7 233 0.9× 124 1.6× 68 1.7× 21 0.6× 53 1.5× 14 358
Junxing Shi United States 6 274 1.0× 123 1.6× 46 1.1× 28 0.8× 49 1.4× 7 370
Yihan Wu United States 7 232 0.9× 63 0.8× 43 1.0× 29 0.8× 15 0.4× 24 356
Kshitij Dwivedi Germany 7 157 0.6× 88 1.1× 63 1.5× 17 0.5× 26 0.7× 16 269
Courtney J. Spoerer United Kingdom 6 429 1.6× 133 1.7× 84 2.0× 11 0.3× 32 0.9× 7 544
Zhijiang Wan China 9 160 0.6× 25 0.3× 39 1.0× 30 0.8× 34 1.0× 22 264
Chang’an A. Zhan China 13 207 0.8× 29 0.4× 56 1.4× 33 0.9× 12 0.3× 30 359
Taku Yoshioka Japan 7 472 1.8× 45 0.6× 81 2.0× 113 3.1× 12 0.3× 16 604
Kuan Han United States 8 129 0.5× 56 0.7× 42 1.0× 20 0.6× 23 0.7× 12 256

Countries citing papers authored by Luca Ambrogioni

Since Specialization
Citations

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

Fields of papers citing papers by Luca Ambrogioni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luca Ambrogioni

This figure shows the co-authorship network connecting the top 25 collaborators of Luca Ambrogioni. A scholar is included among the top collaborators of Luca Ambrogioni 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 Luca Ambrogioni. Luca Ambrogioni 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.
2.
Ambrogioni, Luca, et al.. (2025). Memorization and generalization in generative diffusion under the manifold hypothesis. Journal of Statistical Mechanics Theory and Experiment. 2025(7). 73401–73401.
3.
Ambrogioni, Luca. (2024). In Search of Dispersed Memories: Generative Diffusion Models Are Associative Memory Networks. Entropy. 26(5). 381–381. 8 indexed citations
4.
Ambrogioni, Luca, et al.. (2024). Spontaneous symmetry breaking in generative diffusion models*. Journal of Statistical Mechanics Theory and Experiment. 2024(10). 104025–104025. 5 indexed citations
5.
Ambrogioni, Luca & H. Freyja Ólafsdóttir. (2023). Rethinking the hippocampal cognitive map as a meta-learning computational module. Trends in Cognitive Sciences. 27(8). 702–712. 7 indexed citations
6.
Güçlütürk, Yağmur, Luca Ambrogioni, Gabriëlle Ras, et al.. (2022). Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space. Scientific Reports. 12(1). 141–141. 20 indexed citations
7.
Ambrogioni, Luca, et al.. (2022). Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity. Frontiers in Neuroscience. 16. 940972–940972. 6 indexed citations
8.
Hinne, Max, et al.. (2022). Bayesian model averaging for nonparametric discontinuity design. PLoS ONE. 17(6). e0270310–e0270310.
9.
Ahmad, Nasir, Luca Ambrogioni, & Marcel van Gerven. (2021). \nOvercoming the weight transport problem via spike-timing-dependent weight inference. Radboud Repository (Radboud University). 1 indexed citations
10.
Seeliger, Katja, et al.. (2021). End-to-end neural system identification with neural information flow. PLoS Computational Biology. 17(2). e1008558–e1008558. 17 indexed citations
11.
Ahmad, Nasir, Marcel van Gerven, & Luca Ambrogioni. (2020). GAIT-prop: A biologically plausible learning rule derived from backpropagation of error. Neural Information Processing Systems. 33. 10913–10923. 1 indexed citations
12.
Freudenburg, Zachary V., et al.. (2020). Cortical network responses map onto data-driven features that capture visual semantics of movie fragments. Scientific Reports. 10(1). 12077–12077. 6 indexed citations
13.
Ambrogioni, Luca & Eric Maris. (2019). Complex-valued Gaussian process regression for time series analysis. Radboud Repository (Radboud University). 29 indexed citations
14.
Seeliger, Katja, Luca Ambrogioni, Umut Güçlü, & Marcel van Gerven. (2019). Neural Information Flow: Learning neural information processing systems from brain activity. Data Archiving and Networked Services (DANS). 1 indexed citations
15.
Ambrogioni, Luca, Umut Güçlü, Yağmur Güçlütürk, et al.. (2018). Wasserstein Variational Inference. UvA-DARE (University of Amsterdam). 31. 2478–2487. 5 indexed citations
16.
Seeliger, Katja, Umut Güçlü, Luca Ambrogioni, Yağmur Güçlütürk, & Marcel van Gerven. (2018). Generative adversarial networks for reconstructing natural images from brain activity. NeuroImage. 181. 775–785. 97 indexed citations
17.
Ambrogioni, Luca, et al.. (2017). Bayesian model ensembling using meta-trained recurrent neural networks. Radboud Repository (Radboud University). 1–5. 1 indexed citations
18.
Tomassini, Alice, Luca Ambrogioni, W. Pieter Medendorp, & Eric Maris. (2017). Theta oscillations locked to intended actions rhythmically modulate perception. eLife. 6. 79 indexed citations
19.
Ambrogioni, Luca, Marcel van Gerven, & Eric Maris. (2017). Dynamic decomposition of spatiotemporal neural signals. PLoS Computational Biology. 13(5). e1005540–e1005540. 7 indexed citations
20.
Hinne, Max, Luca Ambrogioni, Ronald J. Janssen, Tom Heskes, & Marcel van Gerven. (2013). Structurally-informed Bayesian functional connectivity analysis. NeuroImage. 86. 294–305. 36 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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2026