Daniel J. Amit

12.8k total citations · 7 hit papers
84 papers, 8.0k citations indexed

About

Daniel J. Amit is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Daniel J. Amit has authored 84 papers receiving a total of 8.0k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Cognitive Neuroscience, 33 papers in Artificial Intelligence and 32 papers in Electrical and Electronic Engineering. Recurrent topics in Daniel J. Amit's work include Neural dynamics and brain function (51 papers), Advanced Memory and Neural Computing (32 papers) and Neural Networks and Applications (32 papers). Daniel J. Amit is often cited by papers focused on Neural dynamics and brain function (51 papers), Advanced Memory and Neural Computing (32 papers) and Neural Networks and Applications (32 papers). Daniel J. Amit collaborates with scholars based in Israel, Italy and United States. Daniel J. Amit's co-authors include Hanoch Gutfreund, Haim Sompolinsky, Misha Tsodyks, V. Martı́n-Mayor, Stefano Fusi, Luca Peliti, Gianluigi Mongillo, Nicolas Brunel, Giorgio Parisi and Meir Griniasty and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Journal of Neuroscience.

In The Last Decade

Daniel J. Amit

79 papers receiving 7.6k citations

Hit Papers

Modeling Brain Function 1985 2026 1998 2012 1989 1985 1985 1987 2005 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel J. Amit Israel 32 4.5k 3.1k 1.7k 1.7k 1.6k 84 8.0k
J. Leo van Hemmen Germany 40 3.7k 0.8× 1.2k 0.4× 1.2k 0.7× 2.6k 1.5× 533 0.3× 202 6.7k
John Hertz Denmark 33 1.4k 0.3× 1.9k 0.6× 892 0.5× 647 0.4× 3.0k 1.9× 121 7.8k
Haim Sompolinsky Israel 66 10.5k 2.4× 6.2k 2.0× 4.3k 2.4× 3.5k 2.0× 2.5k 1.6× 176 18.2k
T. Geisel Germany 45 2.4k 0.5× 532 0.2× 4.0k 2.3× 702 0.4× 1.2k 0.8× 194 9.2k
William Bialek United States 62 9.9k 2.2× 2.5k 0.8× 3.0k 1.7× 1.6k 0.9× 488 0.3× 153 18.1k
Hanoch Gutfreund Israel 31 1.4k 0.3× 2.0k 0.6× 724 0.4× 825 0.5× 1.1k 0.7× 89 4.4k
Vincent Hakim France 44 1.9k 0.4× 402 0.1× 2.0k 1.1× 481 0.3× 2.0k 1.3× 89 7.2k
L. F. Abbott United States 50 5.5k 1.2× 984 0.3× 2.3k 1.3× 1.9k 1.1× 136 0.1× 121 12.5k
Ido Kanter Israel 38 855 0.2× 2.0k 0.7× 1.7k 1.0× 1.1k 0.6× 535 0.3× 192 4.9k
M. I. Rabinovich United States 50 4.5k 1.0× 996 0.3× 3.5k 2.0× 1.1k 0.6× 162 0.1× 222 8.4k

Countries citing papers authored by Daniel J. Amit

Since Specialization
Citations

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

Fields of papers citing papers by Daniel J. Amit

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel J. Amit. 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 Daniel J. Amit. The network helps show where Daniel J. Amit may publish in the future.

Co-authorship network of co-authors of Daniel J. Amit

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel J. Amit. A scholar is included among the top collaborators of Daniel J. Amit 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 Daniel J. Amit. Daniel J. Amit 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.
Yakovlev, Volodya, Daniel J. Amit, Sandro Romani, & Shaul Hochstein. (2008). Universal Memory Mechanism for Familiarity Recognition and Identification. Journal of Neuroscience. 28(1). 239–248. 16 indexed citations
2.
Orlov, Tanya, Daniel J. Amit, Volodya Yakovlev, Ehud Zohary, & Shaul Hochstein. (2006). Memory of Ordinal Number Categories in Macaque Monkeys. Journal of Cognitive Neuroscience. 18(3). 399–417. 8 indexed citations
3.
Romani, Sandro, Daniel J. Amit, & Gianluigi Mongillo. (2006). Mean-field analysis of selective persistent activity in presence of short-term synaptic depression. Journal of Computational Neuroscience. 20(2). 201–217. 28 indexed citations
4.
Orlov, Tanya, Daniel J. Amit, Volodya Yakovlev, Ehud Zohary, & Shaul Hochstein. (2006). Memory of Ordinal Number Categories in Macaque Monkeys. Journal of Cognitive Neuroscience. 18(3). 399–417. 11 indexed citations
5.
Mongillo, Gianluigi, et al.. (2005). Learning in realistic networks of spiking neurons and spike‐driven plastic synapses. European Journal of Neuroscience. 21(11). 3143–3160. 33 indexed citations
6.
Yakovlev, Volodya, Alberto Bernacchia, Tanya Orlov, Shaul Hochstein, & Daniel J. Amit. (2004). Multi-item Working Memory — A Behavioral Study. Cerebral Cortex. 15(5). 602–615. 14 indexed citations
7.
Mongillo, Gianluigi & Daniel J. Amit. (2001). Oscillations and Irregular Emission in Networks of Linear Spiking Neurons. Journal of Computational Neuroscience. 11(3). 249–261. 8 indexed citations
8.
Amit, Daniel J.. (1998). Simulation in neurobiology: theory or experiment?. Trends in Neurosciences. 21(6). 231–237. 26 indexed citations
9.
Amit, Daniel J.. (1995). The Hebbian paradigm reintegrated: Local reverberations as internal representations. Behavioral and Brain Sciences. 18(4). 617–626. 279 indexed citations
10.
Amit, Daniel J.. (1993). Field Theory, The Renormalization Group and Critical Phenomena. WORLD SCIENTIFIC eBooks. 519 indexed citations breakdown →
11.
Amit, Daniel J., M. R. Evans, & Micha Abeles. (1990). Attractor neural networks with biological probe records. Network Computation in Neural Systems. 1(4). 381–405. 59 indexed citations
12.
Amit, Daniel J., M. R. Evans, & Micha Abeles. (1990). Attractor neural networks with biological probe records. Network Computation in Neural Systems. 1(4). 381–405. 12 indexed citations
13.
Amit, Daniel J.. (1990). Attractor neural networks and biological reality: associative memory and learning. Future Generation Computer Systems. 6(2). 111–119. 5 indexed citations
14.
Amit, Daniel J.. (1989). Modeling Brain Function. 444 indexed citations breakdown →
15.
Amit, Daniel J., Hanoch Gutfreund, & Haim Sompolinsky. (1987). Information storage in neural networks with low levels of activity. Physical review. A, General physics. 35(5). 2293–2303. 234 indexed citations
16.
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 →
17.
Amit, Daniel J., Giorgio Parisi, & Luca Peliti. (1983). Asymptotic behavior of the "true" self-avoiding walk. Physical review. B, Condensed matter. 27(3). 1635–1645. 190 indexed citations
18.
Amit, Daniel J., et al.. (1981). First order transitions induced by fluctuations in general φ4-theories. Annals of Physics. 133(1). 57–78. 48 indexed citations
19.
Amit, Daniel J. & Yadin Y. Goldschmidt. (1978). Crossover at a bicritical point: Asymptotic behavior of a field theory with quadratic symmetry breaking. Annals of Physics. 114(1-2). 356–409. 61 indexed citations
20.
Amit, Daniel J. & Marco Zannetti. (1973). Self-consistent treatment of a phase transition. Journal of Statistical Physics. 9(1). 1–21. 8 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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