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.
Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity
19961.2k citationsHaim Sompolinsky et al.profile →
Query by committee
19921.1k citationsManfred Opper, Haim Sompolinsky et al.profile →
Theory of orientation tuning in visual cortex.
1995749 citationsHaim Sompolinsky et al.Proceedings of the National Academy of Sciencesprofile →
Spin-glass models of neural networks
1985733 citationsDaniel J. Amit, Hanoch Gutfreund et al.Physical review. A, General physicsprofile →
Storing Infinite Numbers of Patterns in a Spin-Glass Model of Neural Networks
1985697 citationsDaniel J. Amit, Hanoch Gutfreund et al.Physical Review Lettersprofile →
Chaos in Random Neural Networks
1988631 citationsHaim Sompolinsky et al.Physical Review Lettersprofile →
Statistical mechanics of neural networks near saturation
1987573 citationsDaniel J. Amit, Hanoch Gutfreund et al.profile →
The tempotron: a neuron that learns spike timing–based decisions
Countries citing papers authored by Haim Sompolinsky
Since
Specialization
Citations
This map shows the geographic impact of Haim Sompolinsky'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 Haim Sompolinsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haim Sompolinsky more than expected).
Fields of papers citing papers by Haim Sompolinsky
This network shows the impact of papers produced by Haim Sompolinsky. 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 Haim Sompolinsky. The network helps show where Haim Sompolinsky may publish in the future.
Co-authorship network of co-authors of Haim Sompolinsky
This figure shows the co-authorship network connecting the top 25 collaborators of Haim Sompolinsky.
A scholar is included among the top collaborators of Haim Sompolinsky 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 Haim Sompolinsky. Haim Sompolinsky is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lee, Sebastian, et al.. (2024). Statistical mechanics of deep learning. Journal of Statistical Mechanics Theory and Experiment. 2024(10). 104007–104007.
Bernstein, Jeremy, Ishita Dasgupta, David Rolnick, & Haim Sompolinsky. (2017). Markov Transitions between Attractor States in a Recurrent Neural Network.. National Conference on Artificial Intelligence.2 indexed citations
5.
Ganguli, Surya & Haim Sompolinsky. (2010). Short-term memory in neuronal networks through dynamical compressed sensing. Neural Information Processing Systems. 23. 667–675.15 indexed citations
6.
Rajan, Kanaka, L. F. Abbott, & Haim Sompolinsky. (2010). Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics. Neural Information Processing Systems. 23. 1975–1983.7 indexed citations
7.
Abbott, L. F., et al.. (2010). Input-dependent Suppression of Chaos in Recurrent Neural Networks. Bulletin of the American Physical Society. 2010.
Loewenstein, Yonatan & Haim Sompolinsky. (2002). Computation by calcium dynamics in single neurons a possible solution to the problem of neural integration. 26613.1 indexed citations
Dietrich, Rainer, Manfred Opper, & Haim Sompolinsky. (2000). Advances in large margin classifiers. Aston Publications Explorer (Aston University).10 indexed citations
14.
Shriki, Oren, Haim Sompolinsky, & Daniel D. Lee. (2000). An Information Maximization Approach to Overcomplete and Recurrent Representations. Scholarly Commons (University of Pennsylvania). 13. 612–618.14 indexed citations
15.
Yoon, Hyoungsoo & Haim Sompolinsky. (1998). The Effect of Correlations on the Fisher Information of Population Codes. Neural Information Processing Systems. 11. 167–173.35 indexed citations
16.
Lee, Daniel D. & Haim Sompolinsky. (1998). Learning a Continuous Hidden Variable Model for Binary Data. Neural Information Processing Systems. 11. 515–521.2 indexed citations
Barkai, Naama, H. Sebastian Seung, & Haim Sompolinsky. (1994). On-line Learning of Dichotomies. neural information processing systems. 7. 303–310.6 indexed citations
19.
Kleinfeld, David & Haim Sompolinsky. (1989). Associative network models for central pattern generators. MIT Press eBooks. 195–246.19 indexed citations
20.
Amit, Daniel J., Hanoch Gutfreund, & Haim Sompolinsky. (1985). Spin-glass models of neural networks. Physical review. A, General physics. 32(2). 1007–1018.733 indexed citations breakdown →
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.