The primary goal of this project is to enhance the parallel capabilities of NEURON;this is motivated by the large scale of biophysically realistic models of brain function in health and disease that investigators are beginning to propose and study. NEURON is an open source simulator of neurons and neural circuits that we have developed and provide freely for OS X, MS Windows, and UNIX/Linux. It simplifies the creation and analysis of neural models for nonspecialists in numerical methods and programming. NEURON runs on workstations, clusters, and massively parallel supercomputers, and model properties may include, but are not limited to, complex branching morphology, multiple channel types, inhomogeneous channel distribution, ionic diffusion, extracellular fields, electronic instrumentation, and artificial spiking cells. Growing numbers of neuroscientists in laboratories around the world are using it to investigate mechanisms that underlie normal and abnormal brain function in a wide range of disorders, e.g. epilepsy, multiple sclerosis, and disorders of learning and memory, as well as how these are affected by therapeutic interventions such as medications and deep brain stimulation (DBS). The problems they are addressing involve ever greater anatomical and biophysical complexity, which puts a premium on execution speed, yet hardware serial execution performance has remained unchanged for more than half a decade. NEURON already supports thread and MPI styles of parallelism for spike coupled networks, gap junctions, and distributed cable models of individual cells, but we have identified three areas where large improvements are still achievable. The first is GPU parallelization, which will allow network models of stylized cells with complex membrane properties to achieve more than an order of magnitude speed increase. The second is parallelization of NEURON's differential-algebraic equation solver, which will enable models involving extracellular fields, reaction-diffusion, and ephaptic interactions between cells to take advantage of multicore and parallel cluster architectures. The third is to enable multithreaded parallelization of model setup. To help evaluate and refine these new features, we will implement a model of DBS, a focus of much experimental and computational research because of its demonstrated and potential clinical utility. The dissemination and support component of this project includes: enhancing NEURON's interoperability with other simulators via NeuroML 2;software maintenance to ensure that users continue to be able to run NEURON with or without Python under all combinations of OS X, MS Windows, UNIX/Linux, with the various versions of Python still in use, on current 32- and 64-bit processor architectures;active support of users through email and the NEURON Forum;creation of new documentation and publication of articles and reports;presenting courses and organizing meetings of NEURON users.

Public Health Relevance

Neuroscientists use computational models of neurons and neural circuits to better understand how the nervous system functions in health and disease. There is a critical need for development of simulation software and user support so that they can run these models efficiently on parallel computers. This project addresses these needs by building on the strengths of the NEURON Simulator, open source software that we have developed and is used widely by neuroscientists.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS011613-38
Application #
8599795
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Liu, Yuan
Project Start
1978-07-01
Project End
2017-12-31
Budget Start
2014-01-01
Budget End
2014-12-31
Support Year
38
Fiscal Year
2014
Total Cost
$295,018
Indirect Cost
$117,830
Name
Yale University
Department
Neurosciences
Type
Schools of Medicine
DUNS #
043207562
City
New Haven
State
CT
Country
United States
Zip Code
06520
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Casaleggio, Aldo; Hines, Michael L; Migliore, Michele (2014) Computational model of erratic arrhythmias in a cardiac cell network: the role of gap junctions. PLoS One 9:e100288
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Yu, Yuguo; McTavish, Thomas S; Hines, Michael L et al. (2013) Sparse distributed representation of odors in a large-scale olfactory bulb circuit. PLoS Comput Biol 9:e1003014
Gulledge, Allan T; Carnevale, Nicholas T; Stuart, Greg J (2012) Electrical advantages of dendritic spines. PLoS One 7:e36007
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Davison, Andrew P; Hines, Michael L; Muller, Eilif (2009) Trends in programming languages for neuroscience simulations. Front Neurosci 3:374-80

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