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.
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
2024
Expression of model ion channel currents generated using neuron in silico software in real time using dynamic clamp
Acharya S, Panama B, Korbel L, Nilsson L, Hines M, Carnevale N, Bett G, Rasmusson R, Nowak M. Expression of model ion channel currents generated using neuron in silico software in real time using dynamic clamp. Biophysical Journal 2024, 123: 532a-533a. DOI: 10.1016/j.bpj.2023.11.3218.Peer-Reviewed Original Research
2023
Input of expressed hERG current into a complex neuron in silico cardiac atrial cell: Implications for cardiotoxicity screening
Nowak M, Korbel L, Kane A, Panama B, Hines M, Carnevale N, Bett G, Rasmusson R. Input of expressed hERG current into a complex neuron in silico cardiac atrial cell: Implications for cardiotoxicity screening. Biophysical Journal 2023, 122: 382a. DOI: 10.1016/j.bpj.2022.11.2097.Peer-Reviewed Original Research
2022
NEURON Simulation Environment
Hines M, Carnevale T, McDougal R. NEURON Simulation Environment. 2022, 2355-2361. DOI: 10.1007/978-1-0716-1006-0_795.Peer-Reviewed Original ResearchNumerical Integration Methods
Hines M, Carnevale T. Numerical Integration Methods. 2022, 2487-2496. DOI: 10.1007/978-1-0716-1006-0_242.Peer-Reviewed Original ResearchSenseLab: 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.Peer-Reviewed Original ResearchModelDB
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.Peer-Reviewed Original ResearchInterfacing a real-time dynamic clamp system with neuron simulation software in living cells
Nowak M, Korbel L, Kane A, Panama B, Hines M, Carnevale N, Bett G, Rasmusson R. Interfacing a real-time dynamic clamp system with neuron simulation software in living cells. Biophysical Journal 2022, 121: 391a. DOI: 10.1016/j.bpj.2021.11.818.Peer-Reviewed Original Research
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.Peer-Reviewed Original ResearchDomain-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.Peer-Reviewed Original Research
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.Peer-Reviewed Original Research
Academic Achievements & Community Involvement
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Contacts
Neuroscience
Yale University School of Medicine, P.O. Box 208001
New Haven, CT 06520
United States