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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Multi-Timescale Memory Dynamics Extend Task Repertoire in a Reinforcement Learning Network With Attention-Gated Memory
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, July 2018
|
| DOI | 10.3389/fncom.2018.00050 |
| Pubmed ID | |
| Authors |
Marco Martinolli, Wulfram Gerstner, Aditya Gilra |
| Abstract |
The interplay of reinforcement learning and memory is at the core of several recent neural network models, such as the Attention-Gated MEmory Tagging (AuGMEnT) model. While successful at various animal learning tasks, we find that the AuGMEnT network is unable to cope with some hierarchical tasks, where higher-level stimuli have to be maintained over a long time, while lower-level stimuli need to be remembered and forgotten over a shorter timescale. To overcome this limitation, we introduce a hybrid AuGMEnT, with leaky (or short-timescale) and non-leaky (or long-timescale) memory units, that allows the exchange of low-level information while maintaining high-level one. We test the performance of the hybrid AuGMEnT network on two cognitive reference tasks, sequence prediction and 12AX. |
X Demographics
The data shown below were collected from the profiles of 22 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 | 14% |
| Japan | 2 | 9% |
| Switzerland | 1 | 5% |
| United Kingdom | 1 | 5% |
| Norway | 1 | 5% |
| Unknown | 14 | 64% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 19 | 86% |
| Scientists | 3 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 9 | 24% |
| Student > Ph. D. Student | 6 | 16% |
| Student > Doctoral Student | 4 | 11% |
| Student > Bachelor | 4 | 11% |
| Researcher | 3 | 8% |
| Other | 2 | 5% |
| Unknown | 9 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 14 | 38% |
| Neuroscience | 5 | 14% |
| Psychology | 4 | 11% |
| Physics and Astronomy | 2 | 5% |
| Linguistics | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 8 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 06 June 2019.
All research outputs
#4,037,708
of 24,998,746 outputs
Outputs from Frontiers in Computational Neuroscience
#177
of 1,435 outputs
Outputs of similar age
#71,514
of 332,414 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#7
of 34 outputs
Altmetric has tracked 24,998,746 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,435 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 well, scoring higher than 87% 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 332,414 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 78% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.