Daniel Ben Dayan Rubin

632 total citations
9 papers, 434 citations indexed

About

Daniel Ben Dayan Rubin is a scholar working on Cognitive Neuroscience, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Daniel Ben Dayan Rubin has authored 9 papers receiving a total of 434 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Cognitive Neuroscience, 7 papers in Electrical and Electronic Engineering and 4 papers in Artificial Intelligence. Recurrent topics in Daniel Ben Dayan Rubin's work include Advanced Memory and Neural Computing (7 papers), Neural dynamics and brain function (6 papers) and Neuroscience and Neural Engineering (2 papers). Daniel Ben Dayan Rubin is often cited by papers focused on Advanced Memory and Neural Computing (7 papers), Neural dynamics and brain function (6 papers) and Neuroscience and Neural Engineering (2 papers). Daniel Ben Dayan Rubin collaborates with scholars based in United States, Switzerland and Israel. Daniel Ben Dayan Rubin's co-authors include Filipp Akopyan, Paul Merolla, John V. Arthur, W. P. Risk, Dharmendra S. Modha, Andrew S. Cassidy, Steve K. Esser, Vitaly Feldman, Emmett McQuinn and Theodore M. Wong and has published in prestigious journals such as SHILAP Revista de lepidopterología, NeuroImage and Biological Cybernetics.

In The Last Decade

Daniel Ben Dayan Rubin

9 papers receiving 426 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Ben Dayan Rubin United States	8	359	232	142	141	21	9	434
Eric Hunsberger Canada	3	269 0.7×	235 1.0×	127 0.9×	85 0.6×	13 0.6×	5	370
Milad Mozafari France	8	328 0.9×	271 1.2×	133 0.9×	94 0.7×	42 2.0×	12	430
Johannes Partzsch Germany	15	511 1.4×	266 1.1×	128 0.9×	260 1.8×	16 0.8×	50	583
Sadique Sheik United States	12	307 0.9×	160 0.7×	152 1.1×	83 0.6×	27 1.3×	24	465
Corey Lammie Australia	12	362 1.0×	142 0.6×	138 1.0×	113 0.8×	46 2.2×	31	511
Davis Barch United States	5	445 1.2×	202 0.9×	221 1.6×	114 0.8×	79 3.8×	7	540
Gregor Lenz France	5	274 0.8×	134 0.6×	126 0.9×	73 0.5×	18 0.9×	12	361
Stefan Scholze Germany	12	345 1.0×	143 0.6×	83 0.6×	166 1.2×	13 0.6×	32	416
Stephen Brink United States	11	374 1.0×	143 0.6×	93 0.7×	123 0.9×	16 0.8×	19	430
Olga Krestinskaya Kazakhstan	12	615 1.7×	182 0.8×	192 1.4×	207 1.5×	48 2.3×	41	719

Countries citing papers authored by Daniel Ben Dayan Rubin

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Ben Dayan Rubin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Ben Dayan Rubin. 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 Ben Dayan Rubin. The network helps show where Daniel Ben Dayan Rubin may publish in the future.

Co-authorship network of co-authors of Daniel Ben Dayan Rubin

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

All Works

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9 of 9 papers shown

Shrestha, Sumit Bam, et al.. (2023). The Intel neuromorphic DNS challenge. SHILAP Revista de lepidopterología. 3(3). 34005–34005. 18 indexed citations

Frady, E. Paxon, Sumit Bam Shrestha, Daniel Ben Dayan Rubin, et al.. (2022). Efficient Neuromorphic Signal Processing with Resonator Neurons. Journal of Signal Processing Systems. 94(10). 917–927. 11 indexed citations

Du, Zidong, Daniel Ben Dayan Rubin, Yunji Chen, et al.. (2015). Neuromorphic accelerators. 494–507. 55 indexed citations

Cassidy, Andrew S., Paul Merolla, John V. Arthur, et al.. (2013). Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores. 1–10. 171 indexed citations

Arthur, John V., Paul Merolla, Filipp Akopyan, et al.. (2012). Building block of a programmable neuromorphic substrate: A digital neurosynaptic core. Zenodo (CERN European Organization for Nuclear Research). 1–8. 103 indexed citations

Rigotti, Mattia, Daniel Ben Dayan Rubin, Sara E. Morrison, C. Daniel Salzman, & Stefano Fusi. (2010). Attractor concretion as a mechanism for the formation of context representations. NeuroImage. 52(3). 833–847. 34 indexed citations

Rubin, Daniel Ben Dayan. (2007). Long memory lifetimes require complex synapses and limited sparseness. Frontiers in Computational Neuroscience. 1. 7–7. 32 indexed citations

Rubin, Daniel Ben Dayan, Giuseppe Baselli, G.F. Inbar, & S. Cerutti. (2004). An adaptive neuro-fuzzy method (ANFIS) for estimating single-trial movement-related potentials. Biological Cybernetics. 91(2). 63–75. 8 indexed citations

Rubin, Daniel Ben Dayan, Elisabetta Chicca, & Giacomo Indiveri. (2004). Firing proprieties of an adaptive analog VLSI neuron. Data Archiving and Networked Services (DANS). 314–327. 2 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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