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Attention Score in Context
| Title |
A network that performs brute-force conversion of a temporal sequence to a spatial pattern: relevance to odor recognition
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, January 2014
|
| DOI | 10.3389/fncom.2014.00108 |
| Pubmed ID | |
| Authors |
Honi Sanders |
| Abstract |
A classic problem in neuroscience is how temporal sequences (TSs) can be recognized. This problem is exemplified in the olfactory system, where an odor is defined by the TS of olfactory bulb (OB) output that occurs during a sniff. This sequence is discrete because the output is subdivided by gamma frequency oscillations. Here we propose a new class of "brute-force" solutions to recognition of discrete sequences. We demonstrate a network architecture in which there are a small number of modules, each of which provides a persistent snapshot of what occurs in a different gamma cycle. The collection of these snapshots forms a spatial pattern (SP) that can be recognized by standard attractor-based network mechanisms. We will discuss the implications of this strategy for recognizing odor-specific sequences generated by the OB. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 33% |
| Ireland | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 2% |
| France | 1 | 2% |
| Germany | 1 | 2% |
| Unknown | 50 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 26% |
| Researcher | 11 | 21% |
| Professor | 5 | 9% |
| Student > Postgraduate | 5 | 9% |
| Student > Master | 5 | 9% |
| Other | 7 | 13% |
| Unknown | 6 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 18 | 34% |
| Neuroscience | 15 | 28% |
| Computer Science | 3 | 6% |
| Psychology | 2 | 4% |
| Physics and Astronomy | 2 | 4% |
| Other | 5 | 9% |
| Unknown | 8 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 07 October 2014.
All research outputs
#13,718,725
of 22,765,347 outputs
Outputs from Frontiers in Computational Neuroscience
#611
of 1,339 outputs
Outputs of similar age
#167,506
of 305,297 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#8
of 16 outputs
Altmetric has tracked 22,765,347 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,339 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 gotten more attention than average, scoring higher than 53% 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 305,297 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.