Example of Frontiers in Neuroinformatics format
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Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format
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Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format Example of Frontiers in Neuroinformatics format
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open access Open Access

Frontiers in Neuroinformatics — Template for authors

Publisher: Frontiers Media
Categories Rank Trend in last 3 yrs
Neuroscience (miscellaneous) #3 of 24 -
Computer Science Applications #129 of 693 down down by 86 ranks
Biomedical Engineering #57 of 229 down down by 25 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 299 Published Papers | 1867 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 09/07/2020
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Related Journals

open access Open Access

Hindawi

Quality:  
High
CiteRatio: 5.4
SJR: 0.33
SNIP: 1.281
open access Open Access
recommended Recommended

Nature

Quality:  
High
CiteRatio: 28.0
SJR: 5.961
SNIP: 3.528
open access Open Access

IEEE

Quality:  
High
CiteRatio: 6.7
SJR: 0.62
SNIP: 1.198

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

6.2

29% from 2019

CiteRatio for Frontiers in Neuroinformatics from 2016 - 2020
Year Value
2020 6.2
2019 4.8
2018 4.3
2017 7.3
2016 6.4
graph view Graph view
table view Table view

1.144

17% from 2019

SJR for Frontiers in Neuroinformatics from 2016 - 2020
Year Value
2020 1.144
2019 1.377
2018 1.885
2017 2.45
2016 2.437
graph view Graph view
table view Table view

1.364

1% from 2019

SNIP for Frontiers in Neuroinformatics from 2016 - 2020
Year Value
2020 1.364
2019 1.344
2018 1.333
2017 1.688
2016 1.542
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 29% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 17% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 1% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Frontiers in Neuroinformatics

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Frontiers Media

Frontiers in Neuroinformatics

Approved by publishing and review experts on SciSpace, this template is built as per for Frontiers in Neuroinformatics formatting guidelines as mentioned in Frontiers Media author instructions. The current version was created on 09 Jul 2020 and has been used by 154 authors to write and format their manuscripts to this journal.

Neuroscience

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Last updated on
09 Jul 2020
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ISSN
1662-5196
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Impact Factor
High - 1.101
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Open Access
No
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Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
frontiersinSCNS_ENG_HUMS
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B 25 (1982) 4515–4532.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.3389/FNINF.2014.00014
Machine learning for neuroimaging with scikit-learn.

Abstract:

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain ... Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain. read more read less

Topics:

Unsupervised learning (67%)67% related to the paper, Supervised learning (59%)59% related to the paper, Functional neuroimaging (55%)55% related to the paper
View PDF
1,418 Citations
open accessOpen access Journal Article DOI: 10.3389/FNINF.2011.00013
Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

Abstract:

Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional... Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient and optimal use of neuroimaging analysis approaches: 1) No uniform access to neuroimaging analysis software and usage information; 2) No framework for comparative algorithm development and dissemination; 3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; 4) Neuroimaging software packages do not address computational efficiency; and 5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is BSD licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research. read more read less

Topics:

Package development process (62%)62% related to the paper, Software (53%)53% related to the paper, Scripting language (52%)52% related to the paper, Python (programming language) (52%)52% related to the paper
View PDF
1,343 Citations
open accessOpen access Journal Article DOI: 10.3389/NEURO.11.010.2008
Generating Stimuli for Neuroscience Using PsychoPy.
Jonathan W. Peirce1

Abstract:

PsychoPy is a software library written in Python, using OpenGL to generate very precise visual stimuli on standard personal computers. It is designed to allow the construction of as wide a variety of neuroscience experiments as possible, with the least effort. By writing scripts in standard Python syntax users can generate an... PsychoPy is a software library written in Python, using OpenGL to generate very precise visual stimuli on standard personal computers. It is designed to allow the construction of as wide a variety of neuroscience experiments as possible, with the least effort. By writing scripts in standard Python syntax users can generate an enormous variety of visual and auditory stimuli and can interact with a wide range of external hardware (enabling its use in fMRI, EEG, MEG etc.). The structure of scripts is simple and intuitive. As a result, new experiments can be written very quickly, and trying to understand a previously written script is easy, even with minimal code comments. PsychoPy can also generate movies and image sequences to be used in demos or simulated neuroscience experiments. This paper describes the range of tools and stimuli that it provides and the environment in which experiments are conducted. read more read less

Topics:

Scripting language (54%)54% related to the paper, Python (programming language) (51%)51% related to the paper
View PDF
1,321 Citations
open accessOpen access Journal Article DOI: 10.3389/FNINF.2014.00008
Dipy, a library for the analysis of diffusion MRI data

Abstract:

Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model th... Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing. read more read less

Topics:

Diffusion MRI (52%)52% related to the paper, Tractography (52%)52% related to the paper
View PDF
935 Citations
open accessOpen access Journal Article DOI: 10.3389/NEURO.11.011.2008
PyNN: A Common Interface for Neuronal Network Simulators.

Abstract:

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. T... Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization, and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN. read more read less

Topics:

Python (programming language) (52%)52% related to the paper
View PDF
716 Citations
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Frontiers in Neuroinformatics format uses frontiersinSCNS_ENG_HUMS citation style.

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Frequently asked questions

1. Can I write Frontiers in Neuroinformatics in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Frontiers in Neuroinformatics guidelines and auto format it.

2. Do you follow the Frontiers in Neuroinformatics guidelines?

Yes, the template is compliant with the Frontiers in Neuroinformatics guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Frontiers in Neuroinformatics?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Frontiers in Neuroinformatics citation style.

4. Can I use the Frontiers in Neuroinformatics templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Frontiers in Neuroinformatics.

5. Can I use a manuscript in Frontiers in Neuroinformatics that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Frontiers in Neuroinformatics that you can download at the end.

6. How long does it usually take you to format my papers in Frontiers in Neuroinformatics?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Frontiers in Neuroinformatics.

7. Where can I find the template for the Frontiers in Neuroinformatics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Frontiers in Neuroinformatics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Frontiers in Neuroinformatics's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Frontiers in Neuroinformatics an online tool or is there a desktop version?

SciSpace's Frontiers in Neuroinformatics is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Frontiers in Neuroinformatics?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Frontiers in Neuroinformatics?”

11. What is the output that I would get after using Frontiers in Neuroinformatics?

After writing your paper autoformatting in Frontiers in Neuroinformatics, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Frontiers in Neuroinformatics's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Frontiers in Neuroinformatics?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Frontiers in Neuroinformatics. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Frontiers in Neuroinformatics?

The 5 most common citation types in order of usage for Frontiers in Neuroinformatics are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Frontiers in Neuroinformatics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Frontiers in Neuroinformatics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Frontiers in Neuroinformatics in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Frontiers in Neuroinformatics Endnote style according to Elsevier guidelines.

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