David Sussillo

9.5k total citations · 4 hit papers
34 papers, 4.4k citations indexed

About

David Sussillo is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, David Sussillo has authored 34 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Cognitive Neuroscience, 14 papers in Artificial Intelligence and 9 papers in Electrical and Electronic Engineering. Recurrent topics in David Sussillo's work include Neural dynamics and brain function (24 papers), Neural Networks and Reservoir Computing (9 papers) and Advanced Memory and Neural Computing (8 papers). David Sussillo is often cited by papers focused on Neural dynamics and brain function (24 papers), Neural Networks and Reservoir Computing (9 papers) and Advanced Memory and Neural Computing (8 papers). David Sussillo collaborates with scholars based in United States, United Kingdom and Belgium. David Sussillo's co-authors include Krishna V. Shenoy, L. F. Abbott, William T. Newsome, Valerio Mante, Omri Barak, Andrew J. Trevelyan, Rafael Yuste, Matthew T. Kaufman, Mark M. Churchland and Sergey D. Stavisky and has published in prestigious journals such as Nature, Cell and Nature Communications.

In The Last Decade

David Sussillo

33 papers receiving 4.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Context-dependent computation by recurrent dynamics in prefrontal cortex

2013 1.0k citations Valerio Mante, David Sussillo et al. Nature profile →
Generating Coherent Patterns of Activity from Chaotic Neural Networks

2009 634 citations David Sussillo, L. F. Abbott Neuron profile →
Inferring single-trial neural population dynamics using sequential auto-encoders

2018 306 citations Sergey D. Stavisky, Jonathan C. Kao et al. profile →
Computation Through Neural Population Dynamics

2020 279 citations Matthew D. Golub, David Sussillo et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David Sussillo United States	23	3.6k	1.3k	1.1k	905	286	34	4.4k
Surya Ganguli United States	37	2.5k 0.7×	1.5k 1.1×	1.5k 1.4×	484 0.5×	434 1.5×	119	5.1k
Carlos D. Brody United States	39	5.5k 1.5×	2.0k 1.5×	584 0.5×	527 0.6×	395 1.4×	73	6.2k
Sophie Denève France	29	3.1k 0.9×	759 0.6×	558 0.5×	671 0.7×	151 0.5×	58	3.6k
Moshe Abeles Israel	30	3.8k 1.1×	2.4k 1.8×	565 0.5×	669 0.7×	191 0.7×	55	4.7k
Tomoki Fukai Japan	36	3.0k 0.8×	2.1k 1.6×	567 0.5×	765 0.8×	589 2.1×	162	4.2k
Christian K. Machens Portugal	27	3.0k 0.9×	1.3k 0.9×	486 0.4×	607 0.7×	247 0.9×	50	3.5k
Jean‐Marc Fellous United States	35	3.5k 1.0×	2.3k 1.7×	477 0.4×	484 0.5×	338 1.2×	90	6.6k
Jonathan W. Pillow United States	34	4.7k 1.3×	2.0k 1.5×	658 0.6×	590 0.7×	564 2.0×	111	5.6k
Romain Brette France	29	2.7k 0.8×	1.7k 1.3×	453 0.4×	1.4k 1.5×	261 0.9×	99	3.6k
David Willshaw United Kingdom	32	2.4k 0.7×	1.6k 1.2×	1.4k 1.3×	694 0.8×	756 2.6×	100	4.6k

Countries citing papers authored by David Sussillo

Since Specialization

Citations

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

Fields of papers citing papers by David Sussillo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Sussillo

This figure shows the co-authorship network connecting the top 25 collaborators of David Sussillo. A scholar is included among the top collaborators of David Sussillo 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 David Sussillo. David Sussillo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Verma, Nikhil, Prakarsh Yadav, Najja J. Marshall, et al.. (2024). Enabling Advanced Interactions through Closed-loop Control of Motor Unit Activity After Tetraplegia. 1–3. 1 indexed citations

DePasquale, Brian, David Sussillo, L. F. Abbott, & Mark M. Churchland. (2023). The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks. Neuron. 111(5). 631–649.e10. 25 indexed citations

Sylwestrak, Emily, YoungJu Jo, Sam Vesuna, et al.. (2022). Cell-type-specific population dynamics of diverse reward computations. Cell. 185(19). 3568–3587.e27. 33 indexed citations

Duncker, Lea, Laura Driscoll, Krishna V. Shenoy, Maneesh Sahani, & David Sussillo. (2020). Organizing recurrent network dynamics by task-computation to enable continual learning. UCL Discovery (University College London). 33. 14387–14397. 20 indexed citations

Musall, Simon, Anne E Urai, David Sussillo, & Anne K. Churchland. (2019). Harnessing behavioral diversity to understand neural computations for cognition. Current Opinion in Neurobiology. 58. 229–238. 31 indexed citations

Golub, Matthew D. & David Sussillo. (2018). FixedPointFinder: A Tensorflow toolbox for identifying and characterizing fixed points in recurrent neural networks. The Journal of Open Source Software. 3(31). 1003–1003. 14 indexed citations

Nayebi, Aran, Daniel M. Bear, Jonas Kubilius, et al.. (2018). Task-driven convolutional recurrent models of the visual system. Lirias (KU Leuven). 31. 5290–5301. 16 indexed citations

Foerster, Jakob, Justin Gilmer, Jascha Sohl‐Dickstein, Jan Chorowski, & David Sussillo. (2017). Input Switched Affine Networks: An RNN Architecture Designed for Interpretability. International Conference on Machine Learning. 1136–1145. 6 indexed citations

Kaufman, Matthew T., Jeffrey Seely, David Sussillo, et al.. (2016). The Largest Response Component in the Motor Cortex Reflects Movement Timing but Not Movement Type. eNeuro. 3(4). ENEURO.0085–16.2016. 133 indexed citations

10.

Sussillo, David, Sergey D. Stavisky, Jonathan C. Kao, Stephen I. Ryu, & Krishna V. Shenoy. (2016). Making brain–machine interfaces robust to future neural variability. Nature Communications. 7(1). 13749–13749. 119 indexed citations

11.

Sussillo, David, Mark M. Churchland, Matthew T. Kaufman, & Krishna V. Shenoy. (2015). A neural network that finds a naturalistic solution for the production of muscle activity. Nature Neuroscience. 18(7). 1025–1033. 297 indexed citations

12.

Sussillo, David. (2014). Random Walks: Training Very Deep Nonlinear Feed-Forward Networks with Smart Initialization.. arXiv (Cornell University). 7 indexed citations

13.

Barak, Omri, David Sussillo, Ranulfo Romo, Misha Tsodyks, & L. F. Abbott. (2013). From fixed points to chaos: Three models of delayed discrimination. Progress in Neurobiology. 103. 214–222. 103 indexed citations

14.

Mante, Valerio, David Sussillo, Krishna V. Shenoy, & William T. Newsome. (2013). Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature. 503(7474). 78–84. 1011 indexed citations breakdown →

15.

Sussillo, David & L. F. Abbott. (2012). Transferring Learning from External to Internal Weights in Echo-State Networks with Sparse Connectivity. PLoS ONE. 7(5). e37372–e37372. 32 indexed citations

16.

Sussillo, David, Paul Nuyujukian, Joline M. Fan, et al.. (2012). A recurrent neural network for closed-loop intracortical brain–machine interface decoders. Journal of Neural Engineering. 9(2). 26027–26027. 123 indexed citations

17.

Sussillo, David & L. F. Abbott. (2009). Generating Coherent Patterns of Activity from Chaotic Neural Networks. Neuron. 63(4). 544–557. 634 indexed citations breakdown →

18.

Sussillo, David, Taro Toyoizumi, & Wolfgang Maass. (2007). Self-Tuning of Neural Circuits Through Short-Term Synaptic Plasticity. Journal of Neurophysiology. 97(6). 4079–4095. 36 indexed citations

19.

Trevelyan, Andrew J., David Sussillo, Brendon O. Watson, & Rafael Yuste. (2006). Modular Propagation of Epileptiform Activity: Evidence for an Inhibitory Veto in Neocortex. Journal of Neuroscience. 26(48). 12447–12455. 254 indexed citations

20.

Sussillo, David, Anshul Kundaje, & Dimitris Anastassiou. (2004). Spectrogram Analysis of Genomes. SHILAP Revista de lepidopterología.

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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