| Title |
Computing Arm Movements with a Monkey Brainet
|
|---|---|
| Published in |
Scientific Reports, July 2015
|
| DOI | 10.1038/srep10767 |
| Pubmed ID | |
| Authors |
Arjun Ramakrishnan, Peter J. Ifft, Miguel Pais-Vieira, Yoon Woo Byun, Katie Z. Zhuang, Mikhail A. Lebedev, Miguel A.L. Nicolelis |
| Abstract |
Traditionally, brain-machine interfaces (BMIs) extract motor commands from a single brain to control the movements of artificial devices. Here, we introduce a Brainet that utilizes very-large-scale brain activity (VLSBA) from two (B2) or three (B3) nonhuman primates to engage in a common motor behaviour. A B2 generated 2D movements of an avatar arm where each monkey contributed equally to X and Y coordinates; or one monkey fully controlled the X-coordinate and the other controlled the Y-coordinate. A B3 produced arm movements in 3D space, while each monkey generated movements in 2D subspaces (X-Y, Y-Z, or X-Z). With long-term training we observed increased coordination of behavior, increased correlations in neuronal activity between different brains, and modifications to neuronal representation of the motor plan. Overall, performance of the Brainet improved owing to collective monkey behaviour. These results suggest that primate brains can be integrated into a Brainet, which self-adapts to achieve a common motor goal. |
X Demographics
The data shown below were collected from the profiles of 194 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 47 | 24% |
| United States | 13 | 7% |
| Brazil | 6 | 3% |
| United Kingdom | 3 | 2% |
| Germany | 3 | 2% |
| Taiwan | 1 | <1% |
| Finland | 1 | <1% |
| France | 1 | <1% |
| Austria | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 117 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 178 | 92% |
| Scientists | 15 | 8% |
| Unknown | 1 | <1% |
Mendeley readers
The data shown below were compiled from readership statistics for 165 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 2% |
| Japan | 3 | 2% |
| United Kingdom | 2 | 1% |
| Brazil | 2 | 1% |
| Portugal | 1 | <1% |
| France | 1 | <1% |
| Germany | 1 | <1% |
| Unknown | 152 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 35 | 21% |
| Researcher | 31 | 19% |
| Student > Master | 24 | 15% |
| Student > Bachelor | 18 | 11% |
| Professor > Associate Professor | 10 | 6% |
| Other | 23 | 14% |
| Unknown | 24 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 27 | 16% |
| Agricultural and Biological Sciences | 25 | 15% |
| Engineering | 25 | 15% |
| Computer Science | 14 | 8% |
| Psychology | 12 | 7% |
| Other | 31 | 19% |
| Unknown | 31 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 404. 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 18 July 2023.
All research outputs
#75,520
of 25,807,758 outputs
Outputs from Scientific Reports
#1,021
of 143,128 outputs
Outputs of similar age
#679
of 276,801 outputs
Outputs of similar age from Scientific Reports
#8
of 2,042 outputs
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