You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
Chapter title |
NatCSNN: A Convolutional Spiking Neural Network for Recognition of Objects Extracted from Natural Images
|
---|---|
Chapter number | 28 |
Book title |
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation
|
Published in |
arXiv, September 2019
|
DOI | 10.1007/978-3-030-30487-4_28 |
Book ISBNs |
978-3-03-030486-7, 978-3-03-030487-4
|
Authors |
Pedro Machado, Georgina Cosma, T. Martin McGinnity, T. M McGinnity, Machado, Pedro, Cosma, Georgina, McGinnity, T. Martin |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 1 | 11% |
Japan | 1 | 11% |
United Kingdom | 1 | 11% |
Netherlands | 1 | 11% |
Unknown | 5 | 56% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 7 | 78% |
Practitioners (doctors, other healthcare professionals) | 1 | 11% |
Scientists | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 20 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 3 | 15% |
Student > Ph. D. Student | 3 | 15% |
Lecturer | 1 | 5% |
Researcher | 1 | 5% |
Professor > Associate Professor | 1 | 5% |
Other | 0 | 0% |
Unknown | 11 | 55% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 8 | 40% |
Engineering | 1 | 5% |
Unknown | 11 | 55% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 September 2019.
All research outputs
#8,317,986
of 25,515,042 outputs
Outputs from arXiv
#147,251
of 924,684 outputs
Outputs of similar age
#137,655
of 353,127 outputs
Outputs of similar age from arXiv
#4,296
of 20,919 outputs
Altmetric has tracked 25,515,042 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 924,684 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 83% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 353,127 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 20,919 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.