Daniel Neil

4.0k citations
27 papers · 2.4k · 2 hit papers · h-index 18

Impact in

Papers in

Daniel Neil

26 papers receiving 2.3k citations

Daniel Neil's Hit Papers

Applications of machine learning to diagnosis and treatment of neurodegenerative diseases 2020 · 350 citations
3500+3+7Years since publication200400600

Peers

Daniel Neil
Comparison fields: 5 of 143
  • Cognitive Neuroscience 1.0k
  • Electrical and Electronic Engineering 1.5k
  • Artificial Intelligence 776
  • Health Informatics 29
  • Cellular and Molecular Neuroscience 370
Replace Katharina Eggensperger with:
Katharina Eggensperger Germany
Mounir Boukadoum Canada
Syed Umar Amin Saudi Arabia
Anthony S. Maida United States
Jonathan Tapson Australia
Jason K. Eshraghian United States
Saeed Mian Qaisar Saudi Arabia
Muhammad Tariq Sadiq China
Siuly Siuly Australia
Reza Ebrahimpour Iran
Daniel Neil relative to Katharina Eggensperger Germany Katharina Eggensperger's profile →
Citations per field
00.5×5.3×
Katharina Eggensperger · 1×
Citations per year

Countries citing papers authored by Daniel Neil

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Neil

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Neil, 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 Daniel Neil Line = papers co-authored together Daniel Neil links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Hit paper breakdown →
2015639
2
Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
Hit paper breakdown →
2020350
3 2013303
4 2014186
5 2016113
6 2018107
7 201874
8 201567
9 201662
10 201560
11 201652
12 201651
13 201751
14 202047
15
Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design
201835
16 201733
17 201629
18 202028
19 201617
20 201815

About Daniel Neil

Daniel Neil is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience, Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 27 papers that have together received 2.4k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (14 papers), CCD and CMOS Imaging Sensors (9 papers), Neural dynamics and brain function (9 papers), Music and Audio Processing (6 papers), Speech and Audio Processing (6 papers), Ferroelectric and Negative Capacitance Devices (6 papers), Neural Networks and Applications (3 papers) and Advanced Neural Network Applications (2 papers). The work is most often cited by research in Cognitive Neuroscience (1.0k citations), Electrical and Electronic Engineering (1.5k citations), Artificial Intelligence (776 citations), Health Informatics (29 citations) and Cellular and Molecular Neuroscience (370 citations). Daniel Neil has collaborated with scholars based in Switzerland, United Kingdom and France. Frequent co-authors include Shih‐Chii Liu, Michael Pfeiffer, Tobi Delbrück, Jonathan Binas, Matthew Cook, Peter U. Diehl, Peter O’Connor, Alix M.B. Lacoste, Amir Saffari and Richard J. Mead. Their work appears in journals such as Frontiers in Neuroscience, Nature Reviews Neurology, Scientific Reports, IEEE Transactions on Very Large Scale Integration (VLSI) Systems and International Conference on Learning Representations.

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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