Browse
Submit Data
Databases
API
Help

Dataset Information

13 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

GeNN: a code generation framework for accelerated brain simulations.

ABSTRACT: Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.

SUBMITTER: Yavuz E

PROVIDER: S-EPMC4703976 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

ANNarchy: a code generation approach to neural simulations on parallel hardware.

Project description:Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions.

| S-EPMC4521356 | biostudies-literature

Integrating comparative modeling and accelerated simulations reveals conformational and energetic basis of actomyosin force generation.

Project description:Muscle contraction is performed by arrays of contractile proteins in the sarcomere. Serious heart diseases, such as cardiomyopathy, can often be results of mutations in myosin and actin. Direct characterization of how small changes in the myosin-actin complex impact its force production remains challenging. Molecular dynamics (MD) simulations, although capable of studying protein structure-function relationships, are limited owing to the slow timescale of the myosin cycle as well as a lack of various intermediate structures for the actomyosin complex. Here, employing comparative modeling and enhanced sampling MD simulations, we show how the human cardiac myosin generates force during the mechanochemical cycle. Initial conformational ensembles for different myosin-actin states are learned from multiple structural templates with Rosetta. This enables us to efficiently sample the energy landscape of the system using Gaussian accelerated MD. Key myosin loop residues, whose substitutions are related to cardiomyopathy, are identified to form stable or metastable interactions with the actin surface. We find that the actin-binding cleft closure is allosterically coupled to the myosin motor core transitions and ATP-hydrolysis product release from the active site. Furthermore, a gate between switch I and switch II is suggested to control phosphate release at the prepowerstroke state. Our approach demonstrates the ability to link sequence and structural information to motor functions.

| S-EPMC9992861 | biostudies-literature

An automatic restoration framework based on GPU-accelerated collateral filtering in brain MR images.

Project description:BackgroundImage restoration is one of the fundamental and essential tasks within image processing. In medical imaging, developing an effective algorithm that can automatically remove random noise in brain magnetic resonance (MR) images is challenging. The collateral filter has been shown a more powerful algorithm than many existing methods. However, the computation of the collateral filter is more time-consuming and the selection of the filter parameters is also laborious. This paper proposes an automatic noise removal system based on the accelerated collateral filter for brain MR images.MethodsTo solve these problems, we first accelerated the collateral filter with parallel computing using the graphics processing unit (GPU) architecture. We adopted the compute unified device architecture (CUDA), an application programming interface for the GPU by NVIDIA, to hasten the computation. Subsequently, the optimal filter parameters were selected and the automation was achieved by artificial neural networks. Specifically, an artificial neural network system associated with image feature analysis was adopted to establish the automatic image restoration framework. The best feature combination was selected by the paired t-test and the sequential forward floating selection (SFFS) methods.ResultsExperimental results indicated that not only did the proposed automatic image restoration algorithm perform dramatically faster than the traditional collateral filter, but it also effectively removed the noise in a wide variety of brain MR images. A speed up gain of 34 was attained to process an MR image, which completed within 0.1 s. Representative illustrations of brain tumor images demonstrated the capability of identifying lesion boundaries, which outperformed many existing methods.ConclusionsWe believe that our accelerated and automated restoration framework is promising for achieving robust filtering in many brain MR image restoration applications.

| S-EPMC6339330 | biostudies-literature

Code sets out framework for "living wills".

Project description: Not available

| S-EPMC1403268 | biostudies-literature

Accelerated molecular dynamics simulations of protein folding.

Project description:Folding of four fast-folding proteins, including chignolin, Trp-cage, villin headpiece and WW domain, was simulated via accelerated molecular dynamics (aMD). In comparison with hundred-of-microsecond timescale conventional molecular dynamics (cMD) simulations performed on the Anton supercomputer, aMD captured complete folding of the four proteins in significantly shorter simulation time. The folded protein conformations were found within 0.2-2.1 Å of the native NMR or X-ray crystal structures. Free energy profiles calculated through improved reweighting of the aMD simulations using cumulant expansion to the second-order are in good agreement with those obtained from cMD simulations. This allows us to identify distinct conformational states (e.g., unfolded and intermediate) other than the native structure and the protein folding energy barriers. Detailed analysis of protein secondary structures and local key residue interactions provided important insights into the protein folding pathways. Furthermore, the selections of force fields and aMD simulation parameters are discussed in detail. Our work shows usefulness and accuracy of aMD in studying protein folding, providing basic references in using aMD in future protein-folding studies.

| S-EPMC4487363 | biostudies-literature

Coupling Accelerated Molecular Dynamics Methods with Thermodynamic Integration Simulations.

Project description:In this work we propose a straightforward and efficient approach to improve accuracy and convergence of free energy simulations in condensed-phase systems. We also introduce a new accelerated Molecular Dynamics (MD) approach in which molecular conformational transitions are accelerated by lowering the energy barriers while the potential surfaces near the minima are left unchanged. All free energy calculations were performed on the propane-to-propane model system. The accuracy of free energy simulations was significantly improved when sampling of internal degrees of freedom of solute was enhanced. However, accurate and converged results were only achieved when the solvent interactions were taken into account in the accelerated MD approaches. The analysis of the distribution of boost potential along the free energy simulations showed that the new accelerated MD approach samples efficiently both low- and high-energy regions of the potential surface. Since this approach also maintains substantial populations in regions near the minima, the statistics are not compromised in the thermodynamic integration calculations, and, as a result, the ensemble average can be recovered.

| S-EPMC2646661 | biostudies-other

Trypsinogen activation as observed in accelerated molecular dynamics simulations.

Project description:Serine proteases are involved in many fundamental physiological processes, and control of their activity mainly results from the fact that they are synthetized in an inactive form that becomes active upon cleavage. Three decades ago Martin Karplus's group performed the first molecular dynamics simulations of trypsin, the most studied member of the serine protease family, to address the transition from the zymogen to its active form. Based on the computational power available at the time, only high frequency fluctuations, but not the transition steps, could be observed. By performing accelerated molecular dynamics (aMD) simulations, an interesting approach that increases the configurational sampling of atomistic simulations, we were able to observe the N-terminal tail insertion, a crucial step of the transition mechanism. Our results also support the hypothesis that the hydrophobic effect is the main force guiding the insertion step, although substantial enthalpic contributions are important in the activation mechanism. As the N-terminal tail insertion is a conserved step in the activation of serine proteases, these results afford new perspective on the underlying thermodynamics of the transition from the zymogen to the active enzyme.

| S-EPMC4241106 | biostudies-literature

Blind protein structure prediction using accelerated free-energy simulations.

Project description:We report a key proof of principle of a new acceleration method [Modeling Employing Limited Data (MELD)] for predicting protein structures by molecular dynamics simulation. It shows that such Boltzmann-satisfying techniques are now sufficiently fast and accurate to predict native protein structures in a limited test within the Critical Assessment of Structure Prediction (CASP) community-wide blind competition.

| S-EPMC5106196 | biostudies-literature

Enhancing protein adsorption simulations by using accelerated molecular dynamics.

Project description:The atomistic modeling of protein adsorption on surfaces is hampered by the different time scales of the simulation ([Formula: see text][Formula: see text]s) and experiment (up to hours), and the accordingly different 'final' adsorption conformations. We provide evidence that the method of accelerated molecular dynamics is an efficient tool to obtain equilibrated adsorption states. As a model system we study the adsorption of the protein BMP-2 on graphite in an explicit salt water environment. We demonstrate that due to the considerably improved sampling of conformational space, accelerated molecular dynamics allows to observe the complete unfolding and spreading of the protein on the hydrophobic graphite surface. This result is in agreement with the general finding of protein denaturation upon contact with hydrophobic surfaces.

| S-EPMC3670854 | biostudies-literature

A Mathematical Model and MATLAB Code for Muscle-Fluid-Structure Simulations.

Project description:This article provides models and code for numerically simulating muscle-fluid-structure interactions (FSIs). This work was presented as part of the symposium on Leading Students and Faculty to Quantitative Biology through Active Learning at the society-wide meeting of the Society for Integrative and Comparative Biology in 2015. Muscle mechanics and simple mathematical models to describe the forces generated by muscular contractions are introduced in most biomechanics and physiology courses. Often, however, the models are derived for simplifying cases such as isometric or isotonic contractions. In this article, we present a simple model of the force generated through active contraction of muscles. The muscles' forces are then used to drive the motion of flexible structures immersed in a viscous fluid. An example of an elastic band immersed in a fluid is first presented to illustrate a fully-coupled FSI in the absence of any external driving forces. In the second example, we present a valveless tube with model muscles that drive the contraction of the tube. We provide a brief overview of the numerical method used to generate these results. We also include as Supplementary Material a MATLAB code to generate these results. The code was written for flexibility so as to be easily modified to many other biological applications for educational purposes.

| S-EPMC6296398 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data