Jonathan Binas

2.1k citations
15 papers · 1.1k · 1 hit paper · h-index 8

Impact in

Papers in

Jonathan Binas

15 papers receiving 1.0k citations

Jonathan Binas's Hit Papers

Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing 2015 · 683 citations
6830+3+7Years since publication200400600

Peers

Jonathan Binas
Comparison fields: 5 of 74
  • Cognitive Neuroscience 560
  • Electrical and Electronic Engineering 875
  • Cellular and Molecular Neuroscience 229
  • Artificial Intelligence 287
  • Computer Vision and Pattern Recognition 78
Replace Andreas Wild with:
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Zhengwu Liu China
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Jonathan Binas relative to Andreas Wild United States Andreas Wild's profile →
Citations per field
00.5×3.9×
Andreas Wild · 1×
Citations per year

Countries citing papers authored by Jonathan Binas

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan Binas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jonathan Binas, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jonathan Binas Line = papers co-authored together Jonathan Binas links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Hit paper breakdown →
2015683
2 2013110
3 2011106
4 202056
5 201736
6 201430
7 201615
8 201811
9
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives
20202
10 20122
11
Out-of-Distribution Generalization via Risk Extrapolation (REx)
20212
12
Extending the Framework of Equilibrium Propagation to General Dynamics
20181
13
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
20191
14 20151
15
Retrieving Signals with Deep Complex Extractors
20191

About Jonathan Binas

Jonathan Binas is a scholar working on Cognitive Neuroscience, Electrical and Electronic Engineering, Artificial Intelligence, Cellular and Molecular Neuroscience and Mechanical Engineering, having authored 15 papers that have together received 1.1k indexed citations. Recurring topics across this work include Neural dynamics and brain function (9 papers), Advanced Memory and Neural Computing (9 papers), Ferroelectric and Negative Capacitance Devices (3 papers), Neural Networks and Reservoir Computing (3 papers), Neural Networks and Applications (2 papers), CCD and CMOS Imaging Sensors (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Neuroscience and Neural Engineering (1 paper). The work is most often cited by research in Cognitive Neuroscience (560 citations), Electrical and Electronic Engineering (875 citations), Cellular and Molecular Neuroscience (229 citations), Artificial Intelligence (287 citations) and Computer Vision and Pattern Recognition (78 citations). Jonathan Binas has collaborated with scholars based in Switzerland, United States and Canada. Frequent co-authors include Shih‐Chii Liu, Daniel Neil, Michael Pfeiffer, Peter U. Diehl, Matthew Cook, Giacomo Indiveri, Ueli Rutishauser, Tobi Delbrück, Elisabetta Chicca and Rodney J. Douglas. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nano Letters, Frontiers in Computational Neuroscience, Lecture notes in computer science and PolyPublie (École Polytechnique de Montréal).

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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