Michael Hines, PhD
Senior Research Scientist in NeuroscienceCards
About
Titles
Senior Research Scientist in Neuroscience
Biography
My interest is in the area of conceptual control of neural modeling. NEURON, a program we have developed and provide freely for Mac OS X, MS Windows, and UNIX, simplifies the creation and analysis of neural model for nonspecialists in numerical methods and programming. It is used by neuroscientists around the world to investigate cellular and network mechanisms that are involved in inborn and acquired disorders such as epilepsy, multiple sclerosis, and disorders of learning and memory, and how they are affected by therapeutic interventions such as medications and deep brain stimulation. With NEURON, investigators can simulate individual cells and networks of neurons on workstations, clusters, and massively parallel supercomputers. 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 neurons.
Last Updated on April 07, 2025.
Appointments
Neuroscience
Senior Research ScientistPrimary
Other Departments & Organizations
- Neuroscience
- Shepherd Lab
Education & Training
- Postdoctoral Fellow
- University of Chicago (1976)
- PhD
- University of Chicago (1975)
- MS
- University of Chicago, Physics (1972)
- BS
- Michigan State University, Physics (1970)
Research
Publications
2022
SenseLab: Integration of Multidisciplinary Neuroscience Data
Shepherd G, Morse T, Marenco L, Cheung K, Carnevale T, Migliore M, McDougal R, Hines M, Miller P. SenseLab: Integration of Multidisciplinary Neuroscience Data. 2022, 3069-3072. DOI: 10.1007/978-1-0716-1006-0_497.BooksModelDB
McDougal R, Wang R, Morse T, Migliore M, Marenco L, Carnevale T, Hines M, Shepherd G. ModelDB. 2022, 2053-2056. DOI: 10.1007/978-1-0716-1006-0_158.BooksNEURON Simulation Environment
Hines M, Carnevale T, McDougal R. NEURON Simulation Environment. 2022, 2355-2361. DOI: 10.1007/978-1-0716-1006-0_795.BooksNumerical Integration Methods
Hines M, Carnevale T. Numerical Integration Methods. 2022, 2487-2496. DOI: 10.1007/978-1-0716-1006-0_242.Books
2020
An Optimizing Multi-platform Source-to-source Compiler Framework for the NEURON MODeling Language
Kumbhar P, Awile O, Keegan L, Alonso J, King J, Hines M, Schürmann F. An Optimizing Multi-platform Source-to-source Compiler Framework for the NEURON MODeling Language. Lecture Notes In Computer Science 2020, 12137: 45-58. PMCID: PMC7302241, DOI: 10.1007/978-3-030-50371-0_4.ChaptersDomain-specific languageDomain-specific optimizationsSource compiler frameworkCode generation frameworkTarget code generationUser modelCompiler frameworkModeling languageCode generationMultiple SIMDModern hardwareTarget architectureParallel simulationGeneration frameworkOverall speedupEfficient codeUser communityOptimized kernelsSpeedupAlgebraic simplificationSoftwareFrameworkCodeProduction simulationKernelFully-Asynchronous Fully-Implicit Variable-Order Variable-Timestep Simulation of Neural Networks
Magalhães B, Hines M, Sterling T, Schürmann F. Fully-Asynchronous Fully-Implicit Variable-Order Variable-Timestep Simulation of Neural Networks. Lecture Notes In Computer Science 2020, 12141: 94-108. PMCID: PMC7302593, DOI: 10.1007/978-3-030-50426-7_8.Chapters
2019
NEURON Simulation Environment
Hines M, Carnevale T, McDougal R. NEURON Simulation Environment. 2019, 1-7. DOI: 10.1007/978-1-4614-7320-6_795-2.BooksFully-Asynchronous Cache-Efficient Simulation of Detailed Neural Networks
Magalhães B, Sterling T, Hines M, Schürmann F. Fully-Asynchronous Cache-Efficient Simulation of Detailed Neural Networks. Lecture Notes In Computer Science 2019, 11538: 421-434. DOI: 10.1007/978-3-030-22744-9_33.ChaptersRuntime systemNeural networkScientific applicationsLarge-scale scientific applicationsAsynchronous runtime systemHPX runtime systemParalleX execution modelOverlap of computationLinear data structuresBetter cache localityCompute architecturesExecution modelParallel executionCore kernelsData structureCache localitySynchronous executionMemory spaceCache levelsNode levelBenchmark resultsExecutionLower timeNumber of timestepsNetworkExploiting Flow Graph of System of ODEs to Accelerate the Simulation of Biologically-Detailed Neural Networks
Magalhães B, Hines M, Sterling T, Schürmann F. Exploiting Flow Graph of System of ODEs to Accelerate the Simulation of Biologically-Detailed Neural Networks. 2019, 00: 176-187. DOI: 10.1109/ipdps.2019.00028.Peer-Reviewed Original ResearchCompute nodesScientific use casesHPX runtime systemParalleX execution modelSingle compute nodeLarge-scale benchmarksGranularity of parallelismCompute architecturesRuntime systemAsynchronous executionExecution modelUse casesData implementationParallel simulatorNeural networkScientific applicationsFlow graphDifferent architecturesStrong scalingCore requirementsConcurrent outputArchitectureFlow dependencyParallelismLarge systemsPerformance Analysis of Computational Neuroscience Software NEURON on Knights Corner Many Core Processors
Kumbhar P, Sivagnanam S, Yoshimoto K, Hines M, Carnevale T, Majumdar A. Performance Analysis of Computational Neuroscience Software NEURON on Knights Corner Many Core Processors. Communications In Computer And Information Science 2019, 964: 67-76. DOI: 10.1007/978-981-13-7729-7_5.ChaptersCore processorsKnights CornerMessage Passing Interface (MPI) libraryHigh performance resourcesLoad imbalance issueHigh-performance computingKnights Landing processorPerformance computingInterface libraryLoad balanceDifferent size problemsSimulation environmentParallel performancePerformance resourcesImbalance issueProcessorsLarge modelsSize problemsPerformance analysisHigh performanceNeuroscience communityPerformance of neuronsNetwork of neuronsComputingParallelization
Academic Achievements & Community Involvement
Get In Touch
Contacts
Mobile Phone Number
Mailing Address
Neuroscience
Yale University School of Medicine, P.O. Box 208001
New Haven, CT 06520
United States