Frontiers in Neuroinformatics Journal Impact Factor & Information

Publisher: Frontiers Research Foundation, Frontiers

Current impact factor: 3.26

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2014 Impact Factor 3.261

Additional details

5-year impact 0.00
Cited half-life 3.80
Immediacy index 0.71
Eigenfactor 0.00
Article influence 0.00
ISSN 1662-5196
OCLC 250621701
Material type Document, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details


  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • On open access repositories
    • Authors retain copyright
    • Creative Commons Attribution License
    • Published source must be acknowledged
    • Publisher's version/PDF may be used
    • Set statement to accompany [This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.]
    • Articles are placed in PubMed Central immediately on behalf of authors.
    • All titles are open access journals
    • Publisher last contacted on 16/07/2015
  • Classification

Publications in this journal

  • Frontiers in Neuroinformatics 11/2015; 9. DOI:10.3389/fninf.2015.00026
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We developed NeuroManager, an object-oriented simulation management software engine for computational neuroscience. NeuroManager automates the workflow of simulation job submissions when using heterogeneous computational resources, simulators, and simulation tasks. The object-oriented approach (1) provides flexibility to adapt to a variety of neuroscience simulators, (2) simplifies the use of heterogeneous computational resources, from desktops to super computer clusters, and (3) improves tracking of simulator/simulation evolution. We implemented NeuroManager in MATLAB, a widely used engineering and scientific language, for its signal and image processing tools, prevalence in electrophysiology analysis, and increasing use in college Biology education. To design and develop NeuroManager we analyzed the workflow of simulation submission for a variety of simulators, operating systems, and computational resources, including the handling of input parameters, data, models, results, and analyses. This resulted in 22 stages of simulation submission workflow. The software incorporates progress notification, automatic organization, labeling, and time-stamping of data and results, and integrated access to MATLAB's analysis and visualization tools. NeuroManager provides users with the tools to automate daily tasks, and assists principal investigators in tracking and recreating the evolution of research projects performed by multiple people. Overall, NeuroManager provides the infrastructure needed to improve workflow, manage multiple simultaneous simulations, and maintain provenance of the potentially large amounts of data produced during the course of a research project.
    Frontiers in Neuroinformatics 11/2015; 9:24. DOI:10.3389/fninf.2015.00024
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Surface visualizations of fMRI provide a comprehensive view of cortical activity. However, surface visualizations are difficult to generate and most common visualization techniques rely on unnecessary interpolation which limits the fidelity of the resulting maps. Furthermore, it is difficult to understand the relationship between flattened cortical surfaces and the underlying 3D anatomy using tools available currently. To address these problems we have developed pycortex, a Python toolbox for interactive surface mapping and visualization. Pycortex exploits the power of modern graphics cards to sample volumetric data on a per-pixel basis, allowing dense and accurate mapping of the voxel grid across the surface. Anatomical and functional information can be projected onto the cortical surface. The surface can be inflated and flattened interactively, aiding interpretation of the correspondence between the anatomical surface and the flattened cortical sheet. The output of pycortex can be viewed using WebGL, a technology compatible with modern web browsers. This allows complex fMRI surface maps to be distributed broadly online without requiring installation of complex software.
    Frontiers in Neuroinformatics 09/2015; 9. DOI:10.3389/fninf.2015.00023
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology.
    Frontiers in Neuroinformatics 09/2015; 9. DOI:10.3389/fninf.2015.00022
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: 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.
    Frontiers in Neuroinformatics 08/2015; 9:19. DOI:10.3389/fninf.2015.00019
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Neurons come in a wide variety of shapes and sizes. In a quest to understand this neuronal diversity, researchers have three-dimensionally traced tens of thousands of neurons; many of these tracings are freely available through online repositories like NeuroMorpho.Org and ModelDB. Tracings can be visualized on the computer screen, used for statistical analysis of the properties of different cell types, used to simulate neuronal behavior, and more. We introduce the use of 3D printing as a technique for visualizing traced morphologies. Our method for generating printable versions of a cell or group of cells is to expand dendrite and axon diameters and then to transform the tracing into a 3D object with a neuronal surface generating algorithm like Constructive Tessellated Neuronal Geometry (CTNG). We show that 3D printed cells can be readily examined, manipulated, and compared with other neurons to gain insight into both the biology and the reconstruction process. We share our printable models in a new database, 3DModelDB, and encourage others to do the same with cells that they generate using our code or other methods. To provide additional context, 3DModelDB provides a simulatable version of each cell, links to papers that use or describe it, and links to associated entries in other databases.
    Frontiers in Neuroinformatics 06/2015; 9:18. DOI:10.3389/fninf.2015.00018
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Most of the software platforms for cellular electrophysiology are limited in terms of flexibility, hardware support, ease of use, or re-configuration and adaptation for non-expert users. Moreover, advanced experimental protocols requiring real-time closed-loop operation to investigate excitability, plasticity, dynamics, are largely inaccessible to users without moderate to substantial computer proficiency. Here we present an approach based on MATLAB/Simulink, exploiting the benefits of LEGO-like visual programming and configuration, combined to a small, but easily extendible library of functional software components. We provide and validate several examples, implementing conventional and more sophisticated experimental protocols such as dynamic-clamp or the combined use of intracellular and extracellular methods, involving closed-loop real-time control. The functionality of each of these examples is demonstrated with relevant experiments. These can be used as a starting point to create and support a larger variety of electrophysiological tools and methods, hopefully extending the range of default techniques and protocols currently employed in experimental labs across the world.
    Frontiers in Neuroinformatics 06/2015; 9:17. DOI:10.3389/fninf.2015.00017
  • Source

    Frontiers in Neuroinformatics 06/2015; 9:15. DOI:10.3389/fninf.2015.00015
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: [This corrects the article on p. 71 in vol. 8, PMID: 25206330.].
    Frontiers in Neuroinformatics 06/2015; 9. DOI:10.3389/fninf.2015.00014
  • Source

    Frontiers in Neuroinformatics 03/2015; 9:5. DOI:10.3389/fninf.2015.00005