AUTHOR=DiGiovanna Jack , Rattanatamrong Prapaporn , Zhao Ming , Mahmoudi Babak , Hermer Linda , Figueiredo Renato , Principe Jose C., Fortes Jose , Sanchez Justin C. TITLE=Cyber-workstation for computational neuroscience JOURNAL=Frontiers in Neuroengineering VOLUME=2 YEAR=2010 URL=https://www.frontiersin.org/journals/neuroengineering/articles/10.3389/neuro.16.017.2009 DOI=10.3389/neuro.16.017.2009 ISSN=1662-6443 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.