Publications de Michael Lee Hines

An Optimizing Multi-platform Source-to-source Compiler Framework for the NEURON MODeling Language

Felix Schürmann, James Gonzalo King, Michael Lee Hines, Pramod Shivaji Kumbhar, Omar Awile, Jorge Blanco Alonso, Liam Roger George Keegan

Domain-specific languages (DSLs) play an increasingly important role in the generation of high performing software. They allow the user to exploit domain knowledge for the generation of more efficient code on target architectures. Here, we describe a new c ...

Springer2020

Fully-Asynchronous Fully-Implicit Variable-Order Variable-Timestep Simulation of Neural Networks

Felix Schürmann, Michael Lee Hines, Bruno Ricardo Da Cunha Magalhães

State-of-the-art simulations of detailed neurons follow the Bulk Synchronous Parallel execution model. Execution is divided in equidistant communication intervals, with parallel neurons interpolation and collective communication guiding synchronization. Su ...

2020

Asynchronous Branch-Paralle Simulation of Detailed Neuron Models

Felix Schürmann, Michael Lee Hines

Simulations of electrical activity of networks of morphologically detailed neuron models allow for a better understanding of the brain. State-of-the-art simulations describe the dynamics of ionic currents and biochemical processes within branching topologi ...

2019

Fully-Asynchronous Cache-Efficient Simulation of Detailed Neural Networks

Felix Schürmann, Michael Lee Hines

Modern asynchronous runtime systems allow the re-thinking of large-scale scientific applications. With the example of a simulator of morphologically detailed neural networks, we show how detaching from the commonly used bulk-synchronous parallel (BSP) exec ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

Exploiting Flow Graph of System of ODEs to Accelerate the Simulation of Biologically-Detailed Neural Networks

Felix Schürmann, Michael Lee Hines, Bruno Ricardo Da Cunha Magalhães

Exposing parallelism in scientific applications has become a core requirement for efficiently running on modern distributed multicore SIMD compute architectures. The granularity of parallelism that can be attained is a key determinant for the achievable ac ...

IEEE2019

CoreNEURON : An Optimized Compute Engine for the NEURON Simulator

James Gonzalo King, Felix Schürmann, Michael Lee Hines, Fabien Jonathan Delalondre, Aleksandr Ovcharenko, Pramod Shivaji Kumbhar, Jérémy Pierre Benoit Fouriaux

The NEURON simulator has been developed over the past three decades and is widely used by neuroscientists to model the electrical activity of neuronal networks. Large network simulation projects using NEURON have supercomputer allocations that individually ...

2019

Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits

Werner Alfons Hilda Van Geit, Michael Lee Hines, András Ecker, Sergio Solinas, Matteo Cantarelli

Computational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven models of neural circuits that span multiple scales are increasingly being used to understand brain function in health and dise ...

2019

Code Generation in Computational Neuroscience: A Review of Tools and Techniques

Michael Lee Hines, Pramod Shivaji Kumbhar, Romain Brette

Advances in experimental techniques and computational power allowing researchers to gather anatomical and electrophysiological data at unprecedented levels of detail have fostered the development of increasingly complex models in computational neuroscience ...

2018

Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON

Felix Schürmann, Michael Lee Hines

Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotech ...

Mit Press2016

Leveraging a Cluster-Booster Architecture for Brain-Scale Simulations

James Gonzalo King, Felix Schürmann, Michael Lee Hines, Pramod Shivaji Kumbhar, Aleksandr Ovcharenko

The European Dynamical Exascale Entry Platform (DEEP) is an example of a new type of heterogeneous supercomputing architecture that include both a standard multicore-based "Cluster" used to run less scalable parts of an application, and an Intel MIC-based ...

Springer Int Publishing Ag2016