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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computational Foundations of Natural Intelligence
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, December 2017
|
| DOI | 10.3389/fncom.2017.00112 |
| Pubmed ID | |
| Authors |
Marcel van Gerven |
| Abstract |
New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence. |
X Demographics
The data shown below were collected from the profiles of 24 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 13% |
| Netherlands | 3 | 13% |
| France | 2 | 8% |
| United Kingdom | 1 | 4% |
| Germany | 1 | 4% |
| Turkey | 1 | 4% |
| Switzerland | 1 | 4% |
| Mexico | 1 | 4% |
| Slovakia | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 9 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 18 | 75% |
| Scientists | 5 | 21% |
| Practitioners (doctors, other healthcare professionals) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 284 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 284 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 55 | 19% |
| Researcher | 45 | 16% |
| Student > Master | 45 | 16% |
| Student > Bachelor | 38 | 13% |
| Other | 17 | 6% |
| Other | 44 | 15% |
| Unknown | 40 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 70 | 25% |
| Neuroscience | 59 | 21% |
| Psychology | 24 | 8% |
| Engineering | 21 | 7% |
| Agricultural and Biological Sciences | 19 | 7% |
| Other | 45 | 16% |
| Unknown | 46 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 14 July 2020.
All research outputs
#2,468,048
of 25,035,235 outputs
Outputs from Frontiers in Computational Neuroscience
#96
of 1,436 outputs
Outputs of similar age
#53,436
of 451,885 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#5
of 25 outputs
Altmetric has tracked 25,035,235 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,436 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has done particularly well, scoring higher than 93% 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 451,885 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 88% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.