| 1. |
- Brandi, Maya, et al.
(författare)
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Multiscale modeling through MUSIC multi-simulation : Modeling a dendritic spine using MOOSE and NeuroRD
- 2011
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Ingår i: Front. Neuroinform. Conference Abstract. - : Frontiers Media SA.
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Konferensbidrag (refereegranskat)abstract
- The nervous system encompasses structure and phenomena at different spatial and temporal scales from molecule to behavior. In addition, different scales are described by different physical and mathematical formalisms. The dynamics of second messenger pathways can be formulated as stochastic reaction-diffusion systems [1] while the electrical dynamics of the neuronal membrane is often described by compartment models and the Hodgkin-Huxley formalism. In neuroscience, there is an increasing need and interest to study multi-scale phenomena where multiple scales and physical formalisms are covered by a single model. While there exists simulators/frameworks, such as GENESIS and MOOSE [3], which span such scales (kinetikit/HH-models), most software applications are specialized for a given domain. Here, we report about initial steps towards a framework for multi-scale modeling which builds on the concept of multi-simulations [2]. We aim to provide a standard API and communication framework allowing parallel simulators targeted at different scales and/or different physics to communicate on-line in a cluster environment. Specifically, we show prototype work on simulating the effect on receptor induced cascades on membrane excitability. Electrical properties of a compartment model is simulated in MOOSE, while receptor induced cascades are simulated in NeuroRD [4,7] . In a prototype system, the two simulators are connected using PyMOOSE [5] and JPype [6]. The two models with their different physical properties (membrane currents in MOOSE, molecular biophysics in NeuroRD), are joined into a single model system. We demonstrate the interaction of the numerical solvers of two simulators (MOOSE, NeuroRD) targeted at different spatiotemporal scales and different physics while solving a multi-scale problem. We analyze some of the problems that may arise in multi-scale multi-simulations and present requirements for a generic API for parallel solvers. This work represents initial steps towards a flexible modular framework for simulation of large-scale multi-scale multi-physics problems in neuroscience. References 1. Blackwell KT: An efficient stochastic diffusion algorithm for modeling second messengers in dendrites and spines. J Neurosci Meth 2006, 157: 142-153. 2. Djurfeldt M, Hjorth J, Eppler JM, Dudani N, Helias M, Potjans TC, Bhalla US, Diesmann M, Hellgren Kotaleski J, Ekeberg Ö: Run-Time Interoperability Between Neural Network Simulators Based on the MUSIC Framework. Neurinform 2010, 8: 43-60. 3. Dudani N, Ray S, George S, Bhalla US: Multiscale modeling and interoperability in MOOSE. Neuroscience 2009, 10(Suppl 1): 54. 4. Oliveira RF, Terrin A, Di Benedetto G, Cannon RC, Koh W, Kim M, Zaccolo M, Blacwell KT: The Role of Type 4 Phosphdiesterases in Generating Microdomains of cAMP: Large Scale Stochastic Simulations.
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| 2. |
- Djurfeldt, Mikael, et al.
(författare)
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Efficient generation of connectivity in neuronal networks from simulator-independent descriptions
- 2014
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Ingår i: Frontiers in Neuroinformatics. - : Frontiers Media SA. - 1662-5196. ; 8, s. 43-
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Tidskriftsartikel (refereegranskat)abstract
- Simulator-independent descriptions of connectivity in neuronal networks promise greater ease of model sharing, improved reproducibility of simulation results, and reduced programming effort for computational neuroscientists. However, until now, enabling the use of such descriptions in a given simulator in a computationally efficient way has entailed considerable work for simulator developers, which must be repeated for each new connectivity-generating library that is developed. We have developed a generic connection generator interface that provides a standard way to connect a connectivity-generating library to a simulator, such that one library can easily be replaced by another, according to the modeler's needs. We have used the connection generator interface to connect C++ and Python implementations of the previously described connection-set algebra to the NEST simulator. We also demonstrate how the simulator-independent modeling framework PyNN can transparently take advantage of this, passing a connection description through to the simulator layer for rapid processing in C++ where a simulator supports the connection generator interface and falling-back to slower iteration in Python otherwise. A set of benchmarks demonstrates the good performance of the interface.
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| 3. |
- Eklund, Anders, et al.
(författare)
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BROCCOLI : Software for fast fMRI analysis on many-core CPUs and GPUs
- 2014
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Ingår i: Frontiers in Neuroinformatics. - : Progressive Frontiers Press. - 1662-5196. ; 8:24
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Tidskriftsartikel (refereegranskat)abstract
- Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful graphics processing units (GPUs) to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further, dramatic speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm3 brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github (https://github.com/wanderine/BROCCOLI/).
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| 4. |
- Hansson, Kristin, et al.
(författare)
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RipleyGUI : software for analyzing spatial patterns in 3D cell distributions
- 2013
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Ingår i: Frontiers in Neuroinformatics. - : Frontiers Media SA. - 1662-5196. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- The true revolution in the age of digital neuroanatomy is the ability to extensively quantify anatomical structures and thus investigate structure-function relationships in great detail. To facilitate the quantification of neuronal cell patterns we have developed RipleyGUI, a MATLAB-based software that can be used to detect patterns in the 3D distribution of cells. RipleyGUI uses Ripley's K-function to analyze spatial distributions. In addition the software contains statistical tools to determine quantitative statistical differences, and tools for spatial transformations that are useful for analyzing non-stationary point patterns. The software has a graphical user interface making it easy to use without programming experience, and an extensive user manual explaining the basic concepts underlying the different statistical tools used to analyze spatial point patterns. The described analysis tool can be used for determining the spatial organization of neurons that is important for a detailed study of structure function relationships. For example, neocortex that can be subdivided into six layers based on cell density and cell types can also be analyzed in terms of organizational principles distinguishing the layers.
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| 5. |
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| 6. |
- Lindén, Henrik, et al.
(författare)
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LFPy : A tool for biophysical simulation of extracellular potentials generated by detailed model neurons
- 2014
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Ingår i: Frontiers in Neuroinformatics. - : Frontiers Media SA. - 1662-5196. ; 7:Jan, s. 41-
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Tidskriftsartikel (refereegranskat)abstract
- Electrical extracellular recordings, i.e., recordings of the electrical potentials in the extracellular medium between cells, have been a main work-horse in electrophysiology for almost a century. The high-frequency part of the signal (>500 Hz), i.e., the multi-unit activity (MUA), contains information about the firing of action potentials in surrounding neurons, while the low-frequency part, the local field potential (LFP), contains information about how these neurons integrate synaptic inputs. As the recorded extracellular signals arise from multiple neural processes, their interpretation is typically ambiguous and difficult. Fortunately, a precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established: the extracellular potential can be calculated as a weighted sum of all transmembrane currents in all cells located in the vicinity of the electrode. This computational scheme can considerably aid the modeling and analysis of MUA and LFP signals. Here, we describe LFPy, an open source Python package for numerical simulations of extracellular potentials. LFPy consists of a set of easy-to-use classes for defining cells, synapses and recording electrodes as Python objects, implementing this biophysical modeling scheme. It runs on top of the widely used NEURON simulation environment, which allows for flexible usage of both new and existing cell models. Further, calculation of extracellular potentials using the line-source-method is efficiently implemented. We describe the theoretical framework underlying the extracellular potential calculations and illustrate by examples how LFPy can be used both for simulating LFPs, i.e., synaptic contributions from single cells as well a populations of cells, and MUAs, i.e., extracellular signatures of action potentials.
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| 7. |
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| 8. |
- Nazem, Ali, et al.
(författare)
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Parallel implementation of a biologically inspired model of figure-ground segregation : Application to real-time data using MUSIC
- 2011
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Ingår i: Frontiers in Neuroinformatics. - : Frontiers Research Foundation. - 1662-5196.
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Tidskriftsartikel (refereegranskat)abstract
- MUSIC, the multi-simulation coordinator, supports communication between neuronal-network simulators, or other (parallel) applications, running in a cluster super-computer. Here, we have developed a class library that interfaces between MUSIC-enabled software and applications running on computers outside of the cluster. Specifically, we have used this component to interface the cameras of a robotic head to a neuronal-network simulation running on a Blue Gene/L supercomputer. Additionally, we have developed a parallel implementation of a model for figure ground segregation based on neuronal activity in the Macaque visual cortex. The interface enables the figure ground segregation application to receive real-world images in real-time from the robot. Moreover, it enables the robot to be controlled by the neuronal network.
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| 9. |
- Poline, JB, et al.
(författare)
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Data sharing in neuroimaging research
- 2012
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Ingår i: Frontiers in neuroinformatics. - : Frontiers Media SA. - 1662-5196. ; 6, s. 9-
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Tidskriftsartikel (refereegranskat)
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| 10. |
- Vlachos, Ioannis, et al.
(författare)
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Neural system prediction and identification challenge
- 2013
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Ingår i: Frontiers in Neuroinformatics. - : Frontiers Media SA. - 1662-5196. ; 7:DEC, s. 43-
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Tidskriftsartikel (refereegranskat)abstract
- Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.
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