Abstract
Experiments for the online closed-loop control of neural prosthetics require feedback within 100ms. In a typical neurophysiology laboratory with local computing machines, a majority of this time is spent on acquiring and analyzing the neural signals and a minority (i.e. less than a millisecond) is actual data transfer among machines on local- or campus-area networks. However, the local computing machines may not offer the computational resources necessary for running complex algorithms or scenarios that have been recently proposed. While scientists can take advantage of remote computing resource providers, wide-area networks present much larger latencies that can affect an online experiment. This work presents a split modeling approach that allows the execution of a controller on the neurophysiology resource and the execution of computationally intensive modeling and adaptation algorithms on a remote datacenter, even with the inevitable network latency. Simulation results are presented to quantify how the accuracy of the controller is affected by the split modeling approach in the presence of delays, and to demonstrate that scientists can take advantage of remotely available massive resources.
| Original language | English |
|---|---|
| Title of host publication | 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011 |
| Pages | 453-456 |
| Number of pages | 4 |
| DOIs | |
| State | Published - Jul 20 2011 |
| Event | 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011 - Cancun, Mexico Duration: Apr 27 2011 → May 1 2011 |
Other
| Other | 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011 |
|---|---|
| Country | Mexico |
| City | Cancun |
| Period | 4/27/11 → 5/1/11 |
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ASJC Scopus subject areas
- Neuroscience(all)
Cite this
Towards closed-loop brain-machine experiments across wide-area networks. / Rattanatamrong, Prapaporn; Matsunaga, Andrea; Brockmeier, Austin J.; Sanchez, Justin C.; Principe, Jose C.; Fortes, Jose.
2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011. 2011. p. 453-456 5910584.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Towards closed-loop brain-machine experiments across wide-area networks
AU - Rattanatamrong, Prapaporn
AU - Matsunaga, Andrea
AU - Brockmeier, Austin J.
AU - Sanchez, Justin C.
AU - Principe, Jose C.
AU - Fortes, Jose
PY - 2011/7/20
Y1 - 2011/7/20
N2 - Experiments for the online closed-loop control of neural prosthetics require feedback within 100ms. In a typical neurophysiology laboratory with local computing machines, a majority of this time is spent on acquiring and analyzing the neural signals and a minority (i.e. less than a millisecond) is actual data transfer among machines on local- or campus-area networks. However, the local computing machines may not offer the computational resources necessary for running complex algorithms or scenarios that have been recently proposed. While scientists can take advantage of remote computing resource providers, wide-area networks present much larger latencies that can affect an online experiment. This work presents a split modeling approach that allows the execution of a controller on the neurophysiology resource and the execution of computationally intensive modeling and adaptation algorithms on a remote datacenter, even with the inevitable network latency. Simulation results are presented to quantify how the accuracy of the controller is affected by the split modeling approach in the presence of delays, and to demonstrate that scientists can take advantage of remotely available massive resources.
AB - Experiments for the online closed-loop control of neural prosthetics require feedback within 100ms. In a typical neurophysiology laboratory with local computing machines, a majority of this time is spent on acquiring and analyzing the neural signals and a minority (i.e. less than a millisecond) is actual data transfer among machines on local- or campus-area networks. However, the local computing machines may not offer the computational resources necessary for running complex algorithms or scenarios that have been recently proposed. While scientists can take advantage of remote computing resource providers, wide-area networks present much larger latencies that can affect an online experiment. This work presents a split modeling approach that allows the execution of a controller on the neurophysiology resource and the execution of computationally intensive modeling and adaptation algorithms on a remote datacenter, even with the inevitable network latency. Simulation results are presented to quantify how the accuracy of the controller is affected by the split modeling approach in the presence of delays, and to demonstrate that scientists can take advantage of remotely available massive resources.
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U2 - 10.1109/NER.2011.5910584
DO - 10.1109/NER.2011.5910584
M3 - Conference contribution
SN - 9781424441402
SP - 453
EP - 456
BT - 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011
ER -