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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Functional Subnetwork Approach to Designing Synthetic Nervous Systems That Control Legged Robot Locomotion
|
|---|---|
| Published in |
Frontiers in Neurorobotics, August 2017
|
| DOI | 10.3389/fnbot.2017.00037 |
| Pubmed ID | |
| Authors |
Nicholas S. Szczecinski, Alexander J. Hunt, Roger D. Quinn |
| Abstract |
A dynamical model of an animal's nervous system, or synthetic nervous system (SNS), is a potentially transformational control method. Due to increasingly detailed data on the connectivity and dynamics of both mammalian and insect nervous systems, controlling a legged robot with an SNS is largely a problem of parameter tuning. Our approach to this problem is to design functional subnetworks that perform specific operations, and then assemble them into larger models of the nervous system. In this paper, we present networks that perform addition, subtraction, multiplication, division, differentiation, and integration of incoming signals. Parameters are set within each subnetwork to produce the desired output by utilizing the operating range of neural activity, R, the gain of the operation, k, and bounds based on biological values. The assembly of large networks from functional subnetworks underpins our recent results with MantisBot. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 40% |
| Switzerland | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 35 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 17% |
| Student > Ph. D. Student | 6 | 17% |
| Professor | 3 | 9% |
| Student > Doctoral Student | 2 | 6% |
| Unspecified | 2 | 6% |
| Other | 5 | 14% |
| Unknown | 11 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 14 | 40% |
| Neuroscience | 3 | 9% |
| Unspecified | 2 | 6% |
| Computer Science | 1 | 3% |
| Business, Management and Accounting | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 13 | 37% |
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 17 October 2017.
All research outputs
#13,565,862
of 22,996,001 outputs
Outputs from Frontiers in Neurorobotics
#269
of 876 outputs
Outputs of similar age
#160,752
of 318,007 outputs
Outputs of similar age from Frontiers in Neurorobotics
#7
of 20 outputs
Altmetric has tracked 22,996,001 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 876 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 66% 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 318,007 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 20 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 60% of its contemporaries.