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Digiovanna J, Rattanatamrong P, Zhao M, et al. Cyber-workstation for computational neuroscience. Front Neuroeng. 2010;2:17doi: 10.3389/neuro.16.017.2009.
Digiovanna, J., Rattanatamrong, P., Zhao, M., Mahmoudi, B., Hermer, L., Figueiredo, R., Principe, J. C., Fortes, J., & Sanchez, J. C. (2010). Cyber-workstation for computational neuroscience. Frontiers in neuroengineering, 217. https://doi.org/10.3389/neuro.16.017.2009
Digiovanna, Jack, et al. "Cyber-workstation for computational neuroscience." Frontiers in neuroengineering vol. 2 (2010): 17. doi: https://doi.org/10.3389/neuro.16.017.2009
Digiovanna J, Rattanatamrong P, Zhao M, Mahmoudi B, Hermer L, Figueiredo R, Principe JC, Fortes J, Sanchez JC. Cyber-workstation for computational neuroscience. Front Neuroeng. 2010 Jan 20;2:17. doi: 10.3389/neuro.16.017.2009. eCollection 2010. PMID: 20126436; PMCID: PMC2814557.
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Front Neuroeng. 2010 Jan 20;2:17. doi: 10.3389/neuro.16.017.2009. eCollection 2010.

Cyber-workstation for computational neuroscience.

Frontiers in neuroengineering

Jack Digiovanna, Prapaporn Rattanatamrong, Ming Zhao, Babak Mahmoudi, Linda Hermer, Renato Figueiredo, Jose C Principe, Jose Fortes, Justin C Sanchez

Affiliations

  1. Neuroprosthetics Control Group, ETH Zurich Switzerland.

PMID: 20126436 PMCID: PMC2814557 DOI: 10.3389/neuro.16.017.2009

Abstract

A Cyber-Workstation (CW) to study in vivo, real-time interactions between computational models and large-scale brain subsystems during behavioral experiments has been designed and implemented. The design philosophy seeks to directly link the in vivo neurophysiology laboratory with scalable computing resources to enable more sophisticated computational neuroscience investigation. The architecture designed here allows scientists to develop new models and integrate them with existing models (e.g. recursive least-squares regressor) by specifying appropriate connections in a block-diagram. Then, adaptive middleware transparently implements these user specifications using the full power of remote grid-computing hardware. In effect, the middleware deploys an on-demand and flexible neuroscience research test-bed to provide the neurophysiology laboratory extensive computational power from an outside source. The CW consolidates distributed software and hardware resources to support time-critical and/or resource-demanding computing during data collection from behaving animals. This power and flexibility is important as experimental and theoretical neuroscience evolves based on insights gained from data-intensive experiments, new technologies and engineering methodologies. This paper describes briefly the computational infrastructure and its most relevant components. Each component is discussed within a systematic process of setting up an in vivo, neuroscience experiment. Furthermore, a co-adaptive brain machine interface is implemented on the CW to illustrate how this integrated computational and experimental platform can be used to study systems neurophysiology and learning in a behavior task. We believe this implementation is also the first remote execution and adaptation of a brain-machine interface.

Keywords: brain-machine interface; cyber-workstation; distributed parallel processing; real-time computational neuroscience

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