Cyber-Workstation for Computational Neuroscience
Identifieur interne : 000120 ( Ncbi/Curation ); précédent : 000119; suivant : 000121Cyber-Workstation for Computational Neuroscience
Auteurs : Jack Digiovanna [Suisse] ; Prapaporn Rattanatamrong [États-Unis] ; Ming Zhao [États-Unis] ; Babak Mahmoudi [États-Unis] ; Linda Hermer [États-Unis] ; Renato Figueiredo [États-Unis] ; Jose C. Principe [États-Unis] ; Jose Fortes [États-Unis] ; Justin C. Sanchez [États-Unis]Source :
- Frontiers in Neuroengineering [ 1662-6443 ] ; 2010.
Abstract
A Cyber-Workstation (CW) to study
Url:
DOI: 10.3389/neuro.16.017.2009
PubMed: 20126436
PubMed Central: 2814557
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PMC:2814557Le document en format XML
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<series><title level="j">Frontiers in Neuroengineering</title>
<idno type="eISSN">1662-6443</idno>
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<front><div type="abstract" xml:lang="en"><p>A Cyber-Workstation (CW) to study <italic>in vivo</italic>
, 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 <italic>in vivo</italic>
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 <italic>on-demand and flexible</italic>
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 <italic>in vivo</italic>
, 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.</p>
</div>
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<author><name sortKey="Williams, J C" uniqKey="Williams J">J. C. Williams</name>
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<biblStruct><analytic><author><name sortKey="Zhao, M" uniqKey="Zhao M">M. Zhao</name>
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<author><name sortKey="Rattanatamrong, P" uniqKey="Rattanatamrong P">P. Rattanatamrong</name>
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<author><name sortKey="Mahmoudi, B" uniqKey="Mahmoudi B">B. Mahmoudi</name>
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<author><name sortKey="Sanchez, J C" uniqKey="Sanchez J">J. C. Sanchez</name>
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<author><name sortKey="Fortes, J" uniqKey="Fortes J">J. Fortes</name>
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