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Methods ARTICLE

Cyber-workstation for computational neuroscience

Jack DiGiovanna¹*, Prapaporn Rattanatamrong², Ming Zhao³, Babak Mahmoudi⁴, Linda Hermer⁵, Renato Figueiredo², Jose C. Principe⁶, Jose Fortes² and Justin C. Sanchez⁴

Neuroprosthetics Control Group, ETH Zurich, Switzerland

Advanced Computing & Information Systems Lab, University of Florida, Gainesville, FL, USA

School of Computing & Information Sciences, Florida International University, Miami, FL, USA

Neuroprosthetics Research Group, University of Florida, Gainesville, FL, USA

Department of Psychology, University of Florida, Gainesville, FL, USA

Computational NeuroEngineering Laboratory, University of Florida, Gainesville, FL, USA

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:

cyber-workstation, distributed parallel processing, real-time computational neuroscience, brain-machine interface

Citation:

DiGiovanna J, Rattanatamrong P, Zhao M, Mahmoudi B, Hermer L, Figueiredo R, Principe JC, Fortes J and Sanchez JC (2010). Cyber-workstation for computational neuroscience. Front. Neuroeng. 2:17. doi: 10.3389/neuro.16.017.2009

Received:

29 September 2009;

Paper pending published:

27 October 2009;

Accepted:

07 December 2009;

Published online:

20 January 2010.

Edited by:

Michele Giugliano, Ecole Polytechnique Federale De Lausanne, Switzerland;University of Antwerp, Belgium

Reviewed by:

Alessandro E. P. Villa, Université Joseph Fourier Grenoble, France
Gediminas Luksys, Basel University, Switzerland

© 2010 DiGiovanna, Rattanatamrong, Zhao, Mahmoudi, Hermer, Figueiredo, Principe, Fortes and Sanchez. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.

*Correspondence:

Jack DiGiovanna, Neuroprosthesis Control Group, Automatic Control Lab, Physikstrasse 3, Zürich 8057, Switzerland. e-mail: digiovanna@control.ee.ethz.ch