Sacha J. van Albada

2.5k total citations
46 papers, 1.2k citations indexed

About

Sacha J. van Albada is a scholar working on Cognitive Neuroscience, Electrical and Electronic Engineering and Cellular and Molecular Neuroscience. According to data from OpenAlex, Sacha J. van Albada has authored 46 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Cognitive Neuroscience, 18 papers in Electrical and Electronic Engineering and 9 papers in Cellular and Molecular Neuroscience. Recurrent topics in Sacha J. van Albada's work include Neural dynamics and brain function (38 papers), Functional Brain Connectivity Studies (20 papers) and Advanced Memory and Neural Computing (17 papers). Sacha J. van Albada is often cited by papers focused on Neural dynamics and brain function (38 papers), Functional Brain Connectivity Studies (20 papers) and Advanced Memory and Neural Computing (17 papers). Sacha J. van Albada collaborates with scholars based in Germany, Australia and Netherlands. Sacha J. van Albada's co-authors include P. A. Robinson, Markus Diesmann, Christopher Rennie, Cliff C. Kerr, Maximilian Schmidt, Alan Chiang, Rembrandt Bakker, P. M. Drysdale, Richard T. Gray and Claus C. Hilgetag and has published in prestigious journals such as Nature Communications, ACS Applied Materials & Interfaces and Cerebral Cortex.

In The Last Decade

Sacha J. van Albada

42 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sacha J. van Albada Germany 17 871 395 238 194 78 46 1.2k
Scott L. Brincat United States 20 1.8k 2.0× 521 1.3× 95 0.4× 84 0.4× 45 0.6× 40 2.0k
Philippa J. Karoly Australia 28 2.0k 2.3× 675 1.7× 98 0.4× 251 1.3× 53 0.7× 65 2.6k
Samuel A. Neymotin United States 23 1.0k 1.2× 708 1.8× 227 1.0× 90 0.5× 18 0.2× 60 1.3k
Ankit N. Khambhati United States 21 1.2k 1.4× 465 1.2× 72 0.3× 391 2.0× 164 2.1× 35 1.6k
Natsue Yoshimura Japan 21 695 0.8× 425 1.1× 106 0.4× 56 0.3× 28 0.4× 98 1.3k
Kent Leyde United States 13 1.2k 1.4× 566 1.4× 89 0.4× 226 1.2× 25 0.3× 18 1.5k
Brian DePasquale United States 9 627 0.7× 303 0.8× 171 0.7× 115 0.6× 10 0.1× 14 785
M. D’Havé Belgium 19 890 1.0× 285 0.7× 60 0.3× 211 1.1× 135 1.7× 37 1.2k
Yuko Mizuno‐Matsumoto Japan 16 518 0.6× 160 0.4× 65 0.3× 85 0.4× 34 0.4× 86 732
Dimitris A. Pinotsis United Kingdom 19 855 1.0× 233 0.6× 56 0.2× 39 0.2× 68 0.9× 50 1.1k

Countries citing papers authored by Sacha J. van Albada

Since Specialization
Citations

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

Fields of papers citing papers by Sacha J. van Albada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sacha J. van Albada. 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 Sacha J. van Albada. The network helps show where Sacha J. van Albada may publish in the future.

Co-authorship network of co-authors of Sacha J. van Albada

This figure shows the co-authorship network connecting the top 25 collaborators of Sacha J. van Albada. A scholar is included among the top collaborators of Sacha J. van Albada 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 Sacha J. van Albada. Sacha J. van Albada 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.
Pleßer, Hans Ekkehard, Andrew P. Davison, Markus Diesmann, et al.. (2025). Building on models—a perspective for computational neuroscience. Cerebral Cortex. 35(11). 1 indexed citations
2.
Manninen, Tiina, et al.. (2025). Modeling neuron-astrocyte interactions in neural networks using distributed simulation. PLoS Computational Biology. 21(9). e1013503–e1013503.
3.
Ito, Junji, et al.. (2024). Neural manifolds in V1 change with top-down signals from V4 targeting the foveal region. Cell Reports. 43(7). 114371–114371. 2 indexed citations
4.
5.
Senk, Johanna, Espen Hagen, Sacha J. van Albada, & Markus Diesmann. (2024). Reconciliation of weak pairwise spike–train correlations and highly coherent local field potentials across space. Cerebral Cortex. 34(10). 1 indexed citations
6.
Senden, Mario, Sacha J. van Albada, Giovanni Pezzulo, et al.. (2023). Modular-integrative modeling: a new framework for building brain models that blend biological realism and functional performance. National Science Review. 11(5). nwad318–nwad318.
7.
Albada, Sacha J. van, et al.. (2023). Ubiquitous lognormal distribution of neuron densities in mammalian cerebral cortex. Cerebral Cortex. 33(16). 9439–9449. 4 indexed citations
8.
Chen, Xing, Julia Sprenger, Shashwat Sridhar, et al.. (2022). 1024-channel electrophysiological recordings in macaque V1 and V4 during resting state. Scientific Data. 9(1). 77–77. 13 indexed citations
9.
Senk, Johanna, Birgit Kriener, Mikael Djurfeldt, et al.. (2022). Connectivity concepts in neuronal network modeling. PLoS Computational Biology. 18(9). e1010086–e1010086. 20 indexed citations
10.
Senk, Johanna, Birgit Kriener, Hans Ekkehard Pleßer, et al.. (2019). Connectivity Concepts for Neuronal Networks. JuSER (Forschungszentrum Jülich). 1 indexed citations
11.
Bakker, Rembrandt, et al.. (2019). Multi-area spiking network models of macaque and humancortices. JuSER (Forschungszentrum Jülich).
12.
Schmidt, Maximilian, Rembrandt Bakker, Claus C. Hilgetag, Markus Diesmann, & Sacha J. van Albada. (2017). Multi-scale account of the network structure of macaque visual cortex. Brain Structure and Function. 223(3). 1409–1435. 60 indexed citations
13.
Müller, Eli J., et al.. (2017). Unified neural field theory of brain dynamics underlying oscillations in Parkinson’s disease and generalized epilepsies. Journal of Theoretical Biology. 428. 132–146. 18 indexed citations
14.
Albada, Sacha J. van, et al.. (2016). [Re] Cellular And Network Mechanisms Of Slow Oscillatory Activity (<1 Hz) And Wave Propagations In A Cortical Network Model. Zenodo (CERN European Organization for Nuclear Research). 3 indexed citations
15.
Dahmen, David, et al.. (2014). Computing local field potentials based on spiking cortical networks. Frontiers in Neuroinformatics. 8. 2 indexed citations
16.
Schmidt, Maximilian, Sacha J. van Albada, Jochen Martin Eppler, et al.. (2013). VisNEST &#x2014; Interactive analysis of neural activity data. 65–72. 20 indexed citations
17.
Kerr, Cliff C., Sacha J. van Albada, Samuel A. Neymotin, et al.. (2013). Cortical information flow in Parkinson's disease: a composite network/field model. Frontiers in Computational Neuroscience. 7. 39–39. 34 indexed citations
18.
Albada, Sacha J. van & P. A. Robinson. (2013). Relationships between Electroencephalographic Spectral Peaks Across Frequency Bands. Frontiers in Human Neuroscience. 7. 56–56. 40 indexed citations
19.
Albada, Sacha J. van, Cliff C. Kerr, Alan Chiang, Christopher Rennie, & P. A. Robinson. (2009). Neurophysiological changes with age probed by inverse modeling of EEG spectra. Clinical Neurophysiology. 121(1). 21–38. 60 indexed citations
20.
Albada, Sacha J. van & P. A. Robinson. (2008). Mean-field modeling of the basal ganglia-thalamocortical system. I. Journal of Theoretical Biology. 257(4). 642–663. 116 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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