Davis Barch

1.0k total citations · 1 hit paper
7 papers, 540 citations indexed

About

Davis Barch is a scholar working on Cognitive Neuroscience, Electrical and Electronic Engineering and Cellular and Molecular Neuroscience. According to data from OpenAlex, Davis Barch has authored 7 papers receiving a total of 540 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Cognitive Neuroscience, 4 papers in Electrical and Electronic Engineering and 3 papers in Cellular and Molecular Neuroscience. Recurrent topics in Davis Barch's work include Neural dynamics and brain function (4 papers), Advanced Memory and Neural Computing (4 papers) and Photoreceptor and optogenetics research (2 papers). Davis Barch is often cited by papers focused on Neural dynamics and brain function (4 papers), Advanced Memory and Neural Computing (4 papers) and Photoreceptor and optogenetics research (2 papers). Davis Barch collaborates with scholars based in United States. Davis Barch's co-authors include Andrew S. Cassidy, Alexander Andreopoulos, Myron Flickner, Dharmendra S. Modha, John V. Arthur, Steven K. Esser, Arnon Amir, Paul Merolla, David Van Den Berg and Pallab Datta and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Neurocomputing and IEEE Transactions on Computers.

In The Last Decade

Davis Barch

7 papers receiving 530 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.

Convolutional networks for fast, energy-efficient neuromorphic computing

2016 482 citations Steven K. Esser, Paul Merolla et al. Proceedings of the National Academy of Sciences profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Davis Barch United States	5	445	221	202	114	79	7	540
De Ma China	14	405 0.9×	197 0.9×	210 1.0×	94 0.8×	47 0.6×	44	592
Olga Krestinskaya Kazakhstan	12	615 1.4×	192 0.9×	182 0.9×	207 1.8×	48 0.6×	41	719
Maruan Al-Shedivat United States	9	313 0.7×	165 0.7×	146 0.7×	134 1.2×	42 0.5×	14	421
Corey Lammie Australia	12	362 0.8×	138 0.6×	142 0.7×	113 1.0×	46 0.6×	31	511
Yanqi Chen China	5	451 1.0×	207 0.9×	302 1.5×	60 0.5×	29 0.4×	6	553
Morteza Hosseini United States	10	446 1.0×	223 1.0×	170 0.8×	144 1.3×	71 0.9×	19	607
Arnab Neelim Mazumder United States	8	441 1.0×	207 0.9×	127 0.6×	132 1.2×	65 0.8×	19	578
Massimiliano Versace United States	10	192 0.4×	121 0.5×	255 1.3×	127 1.1×	21 0.3×	24	502
Hakaru Tamukoh Japan	10	360 0.8×	303 1.4×	134 0.7×	49 0.4×	81 1.0×	109	553
Farnood Merrikh-Bayat Iran	12	627 1.4×	153 0.7×	109 0.5×	267 2.3×	27 0.3×	15	689

Countries citing papers authored by Davis Barch

Since Specialization

Citations

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

Fields of papers citing papers by Davis Barch

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Davis Barch

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

All Works

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7 of 7 papers shown

1.

Barch, Davis, Andrew S. Cassidy, Michael DeBole, et al.. (2016). Always-On Speech Recognition Using TrueNorth, a Reconfigurable, Neurosynaptic Processor. IEEE Transactions on Computers. 66(6). 996–1007. 24 indexed citations

2.

Esser, Steven K., Paul Merolla, John V. Arthur, et al.. (2016). Convolutional networks for fast, energy-efficient neuromorphic computing. Proceedings of the National Academy of Sciences. 113(41). 11441–11446. 482 indexed citations breakdown →

3.

Barch, Davis, Andrew S. Cassidy, Michael DeBole, et al.. (2016). LATTE: Low-power Audio Transform with TrueNorth Ecosystem. 4270–4277. 8 indexed citations

4.

Andreopoulos, Alexander, Brian Taba, Andrew S. Cassidy, et al.. (2015). Visual saliency on networks of neurosynaptic cores. IBM Journal of Research and Development. 59(2/3). 9:1–9:16. 12 indexed citations

5.

Barch, Davis & Donald A. Glaser. (2002). Slowly moving stimuli induce characteristic periodic activity waves in an excitable membrane model of visual motion processing. Neurocomputing. 44-46. 43–50. 2 indexed citations

6.

Barch, Davis. (2000). Characterization of activity oscillations in an excitable membrane model and their potential functionality for neuronal computations. Neurocomputing. 32-33. 25–35. 1 indexed citations

7.

Glaser, Donald A. & Davis Barch. (1999). Motion detection and characterization by an excitable membrane: The “bow wave” model. Neurocomputing. 26-27. 137–146. 11 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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