Paul Merolla

9.7k total citations · 4 hit papers
24 papers, 6.4k citations indexed

About

Paul Merolla is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience and Cellular and Molecular Neuroscience. According to data from OpenAlex, Paul Merolla has authored 24 papers receiving a total of 6.4k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Electrical and Electronic Engineering, 16 papers in Cognitive Neuroscience and 14 papers in Cellular and Molecular Neuroscience. Recurrent topics in Paul Merolla's work include Advanced Memory and Neural Computing (22 papers), Neural dynamics and brain function (14 papers) and Neuroscience and Neural Engineering (13 papers). Paul Merolla is often cited by papers focused on Advanced Memory and Neural Computing (22 papers), Neural dynamics and brain function (14 papers) and Neuroscience and Neural Engineering (13 papers). Paul Merolla collaborates with scholars based in United States, Hong Kong and Russia. Paul Merolla's co-authors include John V. Arthur, Dharmendra S. Modha, Rodrigo Alvarez-Icaza, Filipp Akopyan, Rajit Manohar, Nabil Imam, Andrew S. Cassidy, Brian Taba, W. P. Risk and Bryan L. Jackson and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Proceedings of the IEEE.

In The Last Decade

Paul Merolla

24 papers receiving 6.2k citations

Hit Papers

A million spiking-neuron integrated circuit with a scalab... 2014 2026 2018 2022 2014 2015 2014 2016 500 1000 1.5k 2.0k 2.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Paul Merolla United States 17 5.9k 2.3k 2.2k 2.0k 324 24 6.4k
Andrew S. Cassidy United States 18 4.8k 0.8× 1.8k 0.8× 1.7k 0.8× 1.7k 0.8× 287 0.9× 40 5.3k
John V. Arthur United States 21 7.0k 1.2× 3.1k 1.3× 2.9k 1.3× 2.4k 1.2× 352 1.1× 30 7.9k
Brian Taba United States 8 4.8k 0.8× 1.8k 0.8× 1.6k 0.7× 1.7k 0.8× 435 1.3× 8 5.3k
Rodrigo Alvarez-Icaza United States 8 4.6k 0.8× 1.8k 0.7× 1.7k 0.8× 1.6k 0.8× 200 0.6× 11 5.0k
Filipp Akopyan United States 8 4.2k 0.7× 1.5k 0.7× 1.6k 0.7× 1.4k 0.7× 190 0.6× 9 4.5k
Steven K. Esser United States 11 3.6k 0.6× 1.6k 0.7× 1.5k 0.7× 1.3k 0.7× 244 0.8× 12 4.4k
Nabil Imam United States 10 4.0k 0.7× 1.5k 0.6× 1.5k 0.7× 1.4k 0.7× 186 0.6× 16 4.4k
Bernard Brezzo United States 9 3.9k 0.7× 1.3k 0.6× 1.4k 0.6× 1.3k 0.7× 188 0.6× 11 4.3k
Jun Sawada United States 8 3.9k 0.7× 1.3k 0.6× 1.4k 0.6× 1.3k 0.6× 171 0.5× 9 4.2k
Bryan L. Jackson United States 15 4.2k 0.7× 1.4k 0.6× 1.5k 0.7× 1.4k 0.7× 173 0.5× 23 4.8k

Countries citing papers authored by Paul Merolla

Since Specialization
Citations

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

Fields of papers citing papers by Paul Merolla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Merolla

This figure shows the co-authorship network connecting the top 25 collaborators of Paul Merolla. A scholar is included among the top collaborators of Paul Merolla 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 Paul Merolla. Paul Merolla 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.
2.
Andreou, Andreas G., Guillaume Garreau, Garrick Orchard, et al.. (2016). Real-time sensory information processing using the TrueNorth Neurosynaptic System. 2911–2911. 10 indexed citations
3.
Cassidy, Andrew S., Jun Sawada, Paul Merolla, et al.. (2016). TrueNorth: A High-Performance, Low-Power Neurosynaptic Processor for Multi-Sensory Perception, Action, and Cognition. 12 indexed citations
4.
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 →
5.
Akopyan, Filipp, Jun Sawada, Andrew S. Cassidy, et al.. (2015). TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 34(10). 1537–1557. 1064 indexed citations breakdown →
6.
Esser, Steve K., Rathinakumar Appuswamy, Paul Merolla, John V. Arthur, & Dharmendra S. Modha. (2015). Backpropagation for energy-efficient neuromorphic computing. Neural Information Processing Systems. 28. 1117–1125. 170 indexed citations
7.
Das, Srinjoy, Bruno U. Pedroni, Paul Merolla, et al.. (2015). Gibbs sampling with low-power spiking digital neurons. 50. 2704–2707. 9 indexed citations
8.
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
9.
Benjamin, Ben Varkey, Peiran Gao, Emmett McQuinn, et al.. (2014). Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations. Proceedings of the IEEE. 102(5). 699–716. 847 indexed citations breakdown →
10.
Merolla, Paul, John V. Arthur, Rodrigo Alvarez-Icaza, et al.. (2014). A million spiking-neuron integrated circuit with a scalable communication network and interface. Science. 345(6197). 668–673. 2780 indexed citations breakdown →
11.
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
12.
Imam, Nabil, Thomas A. Cleland, Rajit Manohar, et al.. (2012). Implementation of Olfactory Bulb Glomerular-Layer Computations in a Digital Neurosynaptic Core. Frontiers in Neuroscience. 6. 83–83. 27 indexed citations
13.
Imam, Nabil, Filipp Akopyan, John V. Arthur, et al.. (2012). A Digital Neurosynaptic Core Using Event-Driven QDI Circuits. 25–32. 27 indexed citations
14.
Merolla, Paul, John V. Arthur, Filipp Akopyan, et al.. (2011). A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm. 1–4. 315 indexed citations
15.
Merolla, Paul, et al.. (2008). Adaptive Strategies for High Frequency Trading. 1 indexed citations
16.
Merolla, Paul, John V. Arthur, Bertram E. Shi, & Kwabena Boahen. (2007). Expandable Networks for Neuromorphic Chips. IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications. 54(2). 301–311. 69 indexed citations
17.
Merolla, Paul. (2006). A silicon model of the primary visual cortex: Representing features through stochastic variations. Scholarly Commons (University of Pennsylvania). 378. o2038–o2038. 1 indexed citations
18.
Merolla, Paul & Kwabena Boahen. (2006). Dynamic computation in a recurrent network of heterogeneous silicon neurons. 38. 4539–4542. 20 indexed citations
19.
Merolla, Paul, et al.. (2006). Programmable Connections in Neuromorphic Grids. 80–84. 25 indexed citations
20.
Merolla, Paul & Kwabena Boahen. (2003). A Recurrent Model of Orientation Maps with Simple and Complex Cells. ScholarlyCommons (University of Pennsylvania). 16. 995–1002. 42 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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