TY - JOUR
T1 - Cyber-workstation for computational neuroscience
AU - DiGiovanna, Jack
AU - Rattanatamrong, Prapaporn
AU - Zhao, Ming
AU - Mahmoudi, Babak
AU - Hermer, Linda
AU - Figueiredo, Renato
AU - Principe, Jose C.
AU - Fortes, Jose
AU - Sanchez, Justin C.
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2010/1/20
Y1 - 2010/1/20
N2 - 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 specifi cations using the full power of remote grid-computing hardware. In effect, the middleware deploys an on-demand and fl exible 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 fl exibility is important as experimental and theoretical neuroscience evolves based on insights gained from data-intensive experiments, new technologies and engineering methodologies. This paper describes briefl y 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 fi rst remote execution and adaptation of a brain-machine interface.
AB - 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 specifi cations using the full power of remote grid-computing hardware. In effect, the middleware deploys an on-demand and fl exible 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 fl exibility is important as experimental and theoretical neuroscience evolves based on insights gained from data-intensive experiments, new technologies and engineering methodologies. This paper describes briefl y 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 fi rst remote execution and adaptation of a brain-machine interface.
KW - Brain-machine interface
KW - Cyber-workstation
KW - Distributed parallel processing
KW - Real-time computational neuroscience
UR - http://www.scopus.com/inward/record.url?scp=81355152271&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=81355152271&partnerID=8YFLogxK
U2 - 10.3389/neuro.16.011.2009
DO - 10.3389/neuro.16.011.2009
M3 - Article
AN - SCOPUS:81355152271
VL - 2
JO - Frontiers in Neuroengineering
JF - Frontiers in Neuroengineering
SN - 1662-6443
IS - JAN
M1 - 17
ER -