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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Neural Information Processing in Cognition: We Start to Understand the Orchestra, but Where is the Conductor?
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, January 2016
|
| DOI | 10.3389/fncom.2016.00003 |
| Pubmed ID | |
| Authors |
Günther Palm |
| Abstract |
Research in neural information processing has been successful in the past, providing useful approaches both to practical problems in computer science and to computational models in neuroscience. Recent developments in the area of cognitive neuroscience present new challenges for a computational or theoretical understanding asking for neural information processing models that fulfill criteria or constraints from cognitive psychology, neuroscience and computational efficiency. The most important of these criteria for the evaluation of present and future contributions to this new emerging field are listed at the end of this article. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 14% |
| Australia | 1 | 14% |
| Switzerland | 1 | 14% |
| Unknown | 4 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 71% |
| Scientists | 2 | 29% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 4% |
| Portugal | 1 | 2% |
| Unknown | 52 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 27% |
| Student > Master | 8 | 15% |
| Student > Bachelor | 7 | 13% |
| Professor | 5 | 9% |
| Researcher | 5 | 9% |
| Other | 6 | 11% |
| Unknown | 9 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 18 | 33% |
| Psychology | 9 | 16% |
| Computer Science | 7 | 13% |
| Agricultural and Biological Sciences | 6 | 11% |
| Philosophy | 1 | 2% |
| Other | 3 | 5% |
| Unknown | 11 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 05 July 2016.
All research outputs
#5,431,682
of 22,771,140 outputs
Outputs from Frontiers in Computational Neuroscience
#237
of 1,340 outputs
Outputs of similar age
#88,270
of 396,504 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#7
of 30 outputs
Altmetric has tracked 22,771,140 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,340 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done well, scoring higher than 82% 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 396,504 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.