Laurence Aitchison

2.0k total citations
23 papers, 715 citations indexed

About

Laurence Aitchison is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Cellular and Molecular Neuroscience. According to data from OpenAlex, Laurence Aitchison has authored 23 papers receiving a total of 715 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Cognitive Neuroscience, 6 papers in Artificial Intelligence and 5 papers in Cellular and Molecular Neuroscience. Recurrent topics in Laurence Aitchison's work include Neural dynamics and brain function (11 papers), Advanced Memory and Neural Computing (4 papers) and Neural Networks and Applications (4 papers). Laurence Aitchison is often cited by papers focused on Neural dynamics and brain function (11 papers), Advanced Memory and Neural Computing (4 papers) and Neural Networks and Applications (4 papers). Laurence Aitchison collaborates with scholars based in United Kingdom, United States and Denmark. Laurence Aitchison's co-authors include Máté Lengyel, Peter E. Latham, Dan Bang, Bahador Bahrami, Guillaume Hennequin, Rodrigo Echeveste, Santiago Herce Castañón, Alexandre Pouget, Michael Häusser and Ali Mahmoodi and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Neuroscience and Monthly Weather Review.

In The Last Decade

Laurence Aitchison

21 papers receiving 705 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Laurence Aitchison United Kingdom 12 490 110 103 65 65 23 715
Matteo Colombo Netherlands 17 475 1.0× 52 0.5× 58 0.6× 121 1.9× 16 0.2× 66 777
Christopher L. Buckley United Kingdom 17 537 1.1× 71 0.6× 150 1.5× 92 1.4× 51 0.8× 55 903
Christopher H. Donahue United States 11 370 0.8× 78 0.7× 127 1.2× 46 0.7× 42 0.6× 11 568
Natalia I. Córdova United States 7 377 0.8× 52 0.5× 118 1.1× 43 0.7× 19 0.3× 7 538
Nir Levy Israel 8 327 0.7× 119 1.1× 48 0.5× 26 0.4× 88 1.4× 18 413
Peter beim Graben Germany 18 470 1.0× 36 0.3× 230 2.2× 28 0.4× 34 0.5× 52 774
Richard Romero United States 7 634 1.3× 81 0.7× 64 0.6× 46 0.7× 21 0.3× 10 810
Kai-Min Chang United States 10 742 1.5× 21 0.2× 318 3.1× 165 2.5× 34 0.5× 20 1.2k
Masafumi Oizumi Japan 12 776 1.6× 110 1.0× 129 1.3× 59 0.9× 57 0.9× 42 1.1k
Daniel Toker United States 10 275 0.6× 58 0.5× 28 0.3× 40 0.6× 14 0.2× 16 528

Countries citing papers authored by Laurence Aitchison

Since Specialization
Citations

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

Fields of papers citing papers by Laurence Aitchison

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laurence Aitchison

This figure shows the co-authorship network connecting the top 25 collaborators of Laurence Aitchison. A scholar is included among the top collaborators of Laurence Aitchison 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 Laurence Aitchison. Laurence Aitchison 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.
Tonioni, Alessio, et al.. (2025). Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces. Bristol Research (University of Bristol). 1134–1143. 1 indexed citations
2.
Aitchison, Laurence, et al.. (2025). How to Derive Skill from the Fractions Skill Score. Monthly Weather Review. 153(6). 1021–1033.
3.
Tonioni, Alessio, et al.. (2024). TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction Using Vision-Based Tactile Sensing. IEEE Robotics and Automation Letters. 9(6). 5719–5726. 11 indexed citations
4.
O’Donnell, Cian, et al.. (2023). Signatures of Bayesian inference emerge from energy-efficient synapses. eLife. 12. 1 indexed citations
5.
Vosper, Emily, P.A. Watson, Andrew T. T. McRae, et al.. (2023). Deep Learning for Downscaling Tropical Cyclone Rainfall to Hazard‐Relevant Spatial Scales. Journal of Geophysical Research Atmospheres. 128(10). 17 indexed citations
6.
Leech, Gavin, Darren Smith, Joshua Teperowski Monrad, et al.. (2022). Mask wearing in community settings reduces SARS-CoV-2 transmission. Proceedings of the National Academy of Sciences. 119(23). e2119266119–e2119266119. 57 indexed citations
7.
Aitchison, Laurence, et al.. (2021). Synaptic plasticity as Bayesian inference. Nature Neuroscience. 24(4). 565–571. 50 indexed citations
8.
Ünlü, Ali & Laurence Aitchison. (2021). Gradient Regularization as Approximate Variational Inference. Entropy. 23(12). 1629–1629. 2 indexed citations
9.
Fortuin, Vincent, Adrià Garriga-Alonso, Mark van der Wilk, & Laurence Aitchison. (2021). BNNpriors: A library for Bayesian neural network inference with different prior distributions. Software Impacts. 9. 100079–100079. 4 indexed citations
10.
Echeveste, Rodrigo, Laurence Aitchison, Guillaume Hennequin, & Máté Lengyel. (2020). Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference. Nature Neuroscience. 23(9). 1138–1149. 63 indexed citations
11.
Aitchison, Laurence. (2019). Tensor Monte Carlo: Particle Methods for the GPU era. Explore Bristol Research. 32. 7146–7155. 1 indexed citations
12.
Aitchison, Laurence. (2018). A unified theory of adaptive stochastic gradient descent as Bayesian filtering. 1 indexed citations
13.
Aitchison, Laurence, Lloyd Russell, Adam M. Packer, et al.. (2017). Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit. Oxford University Research Archive (ORA) (University of Oxford). 30. 3486–3495. 9 indexed citations
14.
Aitchison, Laurence & Máté Lengyel. (2017). With or without you: predictive coding and Bayesian inference in the brain. Current Opinion in Neurobiology. 46. 219–227. 178 indexed citations
15.
Schmidt‐Hieber, Christoph, Laurence Aitchison, Arnd Roth, et al.. (2017). Active dendritic integration as a mechanism for robust and precise grid cell firing. Nature Neuroscience. 20(8). 1114–1121. 46 indexed citations
16.
Aitchison, Laurence, et al.. (2016). Zipf’s Law Arises Naturally When There Are Underlying, Unobserved Variables. PLoS Computational Biology. 12(12). e1005110–e1005110. 49 indexed citations
17.
Aitchison, Laurence & Máté Lengyel. (2016). The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics. PLoS Computational Biology. 12(12). e1005186–e1005186. 36 indexed citations
18.
Aitchison, Laurence, Dan Bang, Bahador Bahrami, & Peter E. Latham. (2015). Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making. PLoS Computational Biology. 11(10). e1004519–e1004519. 76 indexed citations
19.
Hennequin, Guillaume, Laurence Aitchison, & Máté Lengyel. (2014). Fast Sampling-Based Inference in Balanced Neuronal Networks. Bristol Research (University of Bristol). 27. 2240–2248. 20 indexed citations
20.
Aitchison, Laurence, et al.. (2014). Probabilistic Synapses. arXiv (Cornell University). 3 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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