BrainGrid+Workbench: High-performance/high-quality neural simulation

Availability of affordable hardware that in effect enables desktop supercomputing has enabled more ambitious neural simulations driven by more complex software. However, this opportunity comes with costs, in terms of long learning curves to take advantage of the performance possibilities of idiosyncratic, architecturally heterogenous hardware and decreasing ability to be confident in the quality of simulation results. This paper describes a new neural simulation and software/data provenance framework that reduces the difficulty of taking full advantage of GPU computing and increases investigator confidence that simulations results are valid.

[1]  Dirk Merkel,et al.  Docker: lightweight Linux containers for consistent development and deployment , 2014 .

[2]  David B. Stockton,et al.  NeuroManager: a workflow analysis based simulation management engine for computational neuroscience , 2015, Front. Neuroinform..

[3]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[4]  Susan B. Davidson,et al.  Towards a Model of Provenance and User Views in Scientific Workflows , 2006, DILS.

[5]  Hans Knutsson,et al.  Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates , 2016, Proceedings of the National Academy of Sciences.

[6]  Thomas Nowotny,et al.  GeNN: a code generation framework for accelerated brain simulations , 2016, Scientific Reports.

[7]  Ilkay Altintas,et al.  Provenance Collection Support in the Kepler Scientific Workflow System , 2006, IPAW.

[8]  Arthur P. Goldberg,et al.  Guidelines for Reproducibly Building and Simulating Systems Biology Models , 2016, IEEE Transactions on Biomedical Engineering.

[9]  Luc Moreau,et al.  PROV-Overview. An Overview of the PROV Family of Documents , 2013 .

[10]  Fumitaka Kawasaki Accelerating large-scale simulations of cortical neuronal network development , 2012 .

[11]  Tarek M Taha,et al.  Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors. , 2010, Applied optics.

[12]  Cláudio T. Silva,et al.  Managing Rapidly-Evolving Scientific Workflows , 2006, IPAW.

[13]  Murray Shanahan,et al.  Accelerated simulation of spiking neural networks using GPUs , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[14]  Carole A. Goble,et al.  Using provenance to manage knowledge of In Silico experiments , 2007, Briefings Bioinform..

[15]  Tomoki Fukai,et al.  Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units , 2011, Neural Networks.

[16]  Nikil D. Dutt,et al.  A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors , 2009, Neural Networks.

[17]  James M. Bower,et al.  The book of GENESIS - exploring realistic neural models with the GEneral NEural models SImulation system , 1995 .

[18]  Ian J. Taylor,et al.  Workflows and e-Science: An overview of workflow system features and capabilities , 2009, Future Gener. Comput. Syst..

[19]  Hazeline U. Asuncion,et al.  Using Change Entries to Collect Software Project Information , 2013, SEKE.

[20]  Carole A. Goble,et al.  Taverna, Reloaded , 2010, SSDBM.

[21]  Nikil Dutt,et al.  An efficient automated parameter tuning framework for spiking neural networks , 2014, Front. Neurosci..

[22]  Julien Vitay,et al.  ANNarchy: a code generation approach to neural simulations on parallel hardware , 2015, Front. Neuroinform..

[23]  Nicholas T. Carnevale,et al.  The NEURON Book: Epilogue , 2006 .

[24]  Michael Stiber,et al.  A simple model of cortical culture growth: burst property dependence on network composition and activity , 2014, Biological Cybernetics.

[25]  Michael J. Flynn,et al.  Some Computer Organizations and Their Effectiveness , 1972, IEEE Transactions on Computers.

[26]  David De Roure Towards computational research objects , 2013 .

[27]  E. Burton Swanson,et al.  The dimensions of maintenance , 1976, ICSE '76.

[28]  Bertram Ludäscher,et al.  Scientific workflow design for mere mortals , 2009, Future Gener. Comput. Syst..

[29]  Wojtek Goscinski,et al.  A Tool for Scientific Provenance of Data and Software , 2013, 2013 IEEE 16th International Conference on Computational Science and Engineering.