Daniel Neil

4.0k total citations · 2 hit papers
27 papers, 2.4k citations indexed

About

Daniel Neil is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience and Signal Processing. According to data from OpenAlex, Daniel Neil has authored 27 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Electrical and Electronic Engineering, 11 papers in Cognitive Neuroscience and 8 papers in Signal Processing. Recurrent topics in Daniel Neil's work include Advanced Memory and Neural Computing (14 papers), Neural dynamics and brain function (9 papers) and CCD and CMOS Imaging Sensors (9 papers). Daniel Neil is often cited by papers focused on Advanced Memory and Neural Computing (14 papers), Neural dynamics and brain function (9 papers) and CCD and CMOS Imaging Sensors (9 papers). Daniel Neil collaborates with scholars based in Switzerland, United Kingdom and France. Daniel Neil's co-authors include Shih‐Chii Liu, Michael Pfeiffer, Tobi Delbrück, Jonathan Binas, Peter U. Diehl, Matthew Cook, Peter O’Connor, Alix M.B. Lacoste, Laura Ferraiuolo and Richard J. Mead and has published in prestigious journals such as Scientific Reports, Nature Reviews Neurology and Frontiers in Neuroscience.

In The Last Decade

Daniel Neil

26 papers receiving 2.3k citations

Hit Papers

Fast-classifying, high-accuracy spiking deep networks thr... 2015 2026 2018 2022 2015 2020 200 400 600
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.

  1. Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
    2015 639 citations Peter U. Diehl, Daniel Neil et al. profile →
  2. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
    2020 350 citations Monika A. Myszczynska, Alix M.B. Lacoste et al. Nature Reviews Neurology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Neil Switzerland 18 1.5k 1.0k 776 370 269 27 2.4k
Jonathan Tapson Australia 22 997 0.7× 678 0.7× 1.0k 1.3× 376 1.0× 335 1.2× 118 2.4k
Anthony S. Maida United States 14 786 0.5× 608 0.6× 712 0.9× 188 0.5× 174 0.6× 76 1.7k
Katharina Eggensperger Germany 9 553 0.4× 2.0k 2.0× 750 1.0× 620 1.7× 147 0.5× 12 3.0k
Francisco Pelayo Spain 20 417 0.3× 758 0.7× 312 0.4× 437 1.2× 287 1.1× 79 1.8k
Il Memming Park United States 23 421 0.3× 658 0.7× 475 0.6× 272 0.7× 226 0.8× 78 2.1k
Saeed Reza Kheradpisheh Iran 12 1.4k 0.9× 1.0k 1.0× 673 0.9× 345 0.9× 129 0.5× 27 1.8k
Mounir Boukadoum Canada 23 501 0.3× 398 0.4× 434 0.6× 194 0.5× 311 1.2× 185 1.9k
Ryota Tomioka Japan 24 400 0.3× 2.0k 1.9× 542 0.7× 694 1.9× 320 1.2× 58 3.0k
Sander M. Bohté Netherlands 19 754 0.5× 783 0.8× 447 0.6× 252 0.7× 55 0.2× 67 1.4k
Sanqing Hu China 23 361 0.2× 935 0.9× 375 0.5× 349 0.9× 131 0.5× 71 2.0k

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-authorship network of co-authors of Daniel Neil

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Neil. A scholar is included among the top collaborators of Daniel Neil 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 Neil. Daniel Neil 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.
Myszczynska, Monika A., Alix M.B. Lacoste, Daniel Neil, et al.. (2020). Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nature Reviews Neurology. 16(8). 440–456. 350 indexed citations breakdown →
2.
Paliwal, Saee, et al.. (2020). Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs. Scientific Reports. 10(1). 18250–18250. 28 indexed citations
3.
Neil, Daniel, et al.. (2019). Attention-driven Multi-sensor Selection. Zurich Open Repository and Archive (University of Zurich). 1–8. 3 indexed citations
4.
Neil, Daniel, et al.. (2018). Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design. International Conference on Learning Representations. 35 indexed citations
5.
Moeys, Diederik Paul, Daniel Neil, Federico Corradi, et al.. (2018). PRED18: Dataset and Further Experiments with DAVIS Event Camera in Predator-Prey Robot Chasing. Zurich Open Repository and Archive (University of Zurich). 2 indexed citations
6.
Gao, Chang, Daniel Neil, Enea Ceolini, Shih‐Chii Liu, & Tobi Delbrück. (2018). DeltaRNN. Zurich Open Repository and Archive (University of Zurich). 21–30. 107 indexed citations
7.
Neil, Daniel, et al.. (2018). Feature Representations for Neuromorphic Audio Spike Streams. Frontiers in Neuroscience. 12. 23–23. 74 indexed citations
8.
Neil, Daniel, et al.. (2018). Multi-channel Attention for End-to-End Speech Recognition. Zurich Open Repository and Archive (University of Zurich). 17–21. 15 indexed citations
9.
Binas, Jonathan, Daniel Neil, Shih‐Chii Liu, & Tobi Delbrück. (2017). DDD17: End-To-End DAVIS Driving Dataset. Zurich Open Repository and Archive (University of Zurich). 0–0. 33 indexed citations
10.
Neil, Daniel, et al.. (2017). Live demonstration: Event-driven real-time spoken digit recognition system. 8. 1–1. 1 indexed citations
11.
Neil, Daniel, Michael Pfeiffer, & Shih‐Chii Liu. (2016). Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences. Zurich Open Repository and Archive (University of Zurich). 113 indexed citations
12.
Liu, Hongjie, Diederik Paul Moeys, Gautham P. Das, et al.. (2016). Combined frame- and event-based detection and tracking. Lincoln Repository (University of Lincoln). 2511–2514. 52 indexed citations
13.
Neil, Daniel, et al.. (2016). Event-driven deep neural network hardware system for sensor fusion. Zurich Open Repository and Archive (University of Zurich). 2495–2498. 17 indexed citations
14.
Neil, Daniel, Michael Pfeiffer, & Shih‐Chii Liu. (2016). Learning to be efficient. Zurich Open Repository and Archive (University of Zurich). 293–298. 51 indexed citations
15.
Diehl, Peter U., Daniel Neil, Jonathan Binas, et al.. (2015). Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. 1–8. 639 indexed citations breakdown →
16.
Stromatias, Evangelos, Daniel Neil, Francesco Galluppi, et al.. (2015). Scalable energy-efficient, low-latency implementations of trained spiking Deep Belief Networks on SpiNNaker. Research Explorer (The University of Manchester). 1–8. 60 indexed citations
17.
Stromatias, Evangelos, et al.. (2015). Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms. Frontiers in Neuroscience. 9. 222–222. 67 indexed citations
18.
Stromatias, Evangelos, Daniel Neil, Francesco Galluppi, et al.. (2015). Live demonstration: Handwritten digit recognition using spiking deep belief networks on SpiNNaker. Research Explorer (The University of Manchester). 1901–1901. 8 indexed citations
19.
O’Connor, Peter, Daniel Neil, Shih‐Chii Liu, Tobi Delbrück, & Michael Pfeiffer. (2013). Real-time classification and sensor fusion with a spiking deep belief network. Frontiers in Neuroscience. 7. 178–178. 303 indexed citations
20.
Neil, Daniel, et al.. (1976). Laboratory investigation of "biorhythms".. PubMed. 47(4). 425–9. 5 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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