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

Stiber, Michael; Kawasaki, Fumitaka; Davis, Douglas A.; Asuncion, Hazeline U.; Lee, Jewel Yun-Hsuan; Boyer, Destiny

doi:10.1109/ijcnn.2017.7966156

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Recent progress in electrophysiological recording technology has enabled the number of parallel spike trains to grow rapidly (currently allowing a hundred or more neurons to be simultaneously recorded), doubling every 7 years, mimicking Moore's law [39]). Similarly, the utilization of general purpose graphics processing unit (GPGPU) has allowed neural simulations to model tens of thousands of neurons in extremely long-duration simulations and complete in reasonable time periods (days or weeks, instead of months or years) [40,41,42].…”

Section: Machine Learning Analysismentioning

confidence: 99%

“…The BrainGrid neural simulator [42] was used in this work to produce spike data for a network of 10,000 neurons simulated and recorded for 600 million temp steps with 0.1 ms resolution, that is equivalent to 28 days in vitro. The network is arranged in a 100×100 rectangular grid with inhibitory, excitatory, and endogenously active neurons uniformly distributed; the neuron distribution layout refer to [59].…”

Section: Simulation Configurationmentioning

confidence: 99%

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Machine Learning of Spatiotemporal Bursting Behavior in Developing Neural Networks

Lee

Stiber

2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Computing and Software Systems Experimental investigation of the collective dynamics in large networks of neurons is a fundamental step towards understanding the mechanisms behind signal and information processing in the brain. In the last decade, the emergence of high performance computing technology has allowed long-duration numerical simulations to model large-scale neural networks. These simulated networks exhibit behaviors (ranging from stochastic spiking to synchronized bursting) that are observed in the living preparations. These simulations' high spatiotemporal resolution and long duration produce data that, in terms of both quantity and complexity, challenge our interpretative abilities. This thesis presents an application of machine learning techniques to bridge the gap between microscopic and macroscopic behaviors and identify the small-scale activity that leads to large-scale behavior, reducing data complexity to a level that can be amenable to further analysis.

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Section: Machine Learning Analysismentioning

confidence: 99%

Section: Simulation Configurationmentioning

confidence: 99%

Machine Learning of Spatiotemporal Bursting Behavior in Developing Neural Networks

Lee

Stiber

2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Self Cite

View full text Add to dashboard Cite

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Paradata in Emergency Services Communications Systems

Cohen,

Jordan,

Sotebeer

et al. 2024

Knowledge Management and Organizational Learning

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Government provides a variety of critical services, often grounded in decades-old technology. As these services evolve to encompass newer technologies and offer broader capabilities, their exposure to potential threats increases the importance of modeling and simulation to understand their operation and vulnerabilities. This modeling activity requires access to records collected by government agencies, conceptualized formally in terms of archival science. However, the archival science concept of paradata is insufficient to support the forensic reconstruction of the real world required by a modeling effort. This chapter outlines how iterative, interdisciplinary work to model and simulate emergency services communication systems illuminates the need for a new role for paradata in government archives.

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Software and Data Provenance as a Basis for eScience Workflow

Conquest

Stiber

2021

2021 IEEE 17th International Conference on eScience (eScience)

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

Cited by 3 publications

References 24 publications

Machine Learning of Spatiotemporal Bursting Behavior in Developing Neural Networks

Machine Learning of Spatiotemporal Bursting Behavior in Developing Neural Networks

Paradata in Emergency Services Communications Systems

Software and Data Provenance as a Basis for eScience Workflow

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