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

Frontiers in Neuroscience — Template for authors

Publisher: Frontiers Media
Categories Rank Trend in last 3 yrs
Neuroscience (all) #44 of 110 down down by 15 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 4182 Published Papers | 22692 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 10/06/2020
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Related Journals

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SJR: 1.187
SNIP: 1.192
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SJR: 0.866
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recommended Recommended

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Quality:  
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CiteRatio: 11.0
SJR: 4.127
SNIP: 2.005

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.

5.4

6% from 2019

CiteRatio for Frontiers in Neuroscience from 2016 - 2020
Year Value
2020 5.4
2019 5.1
2018 5.6
2017 5.7
2016 5.6
graph view Graph view
table view Table view

1.499

4% from 2019

SJR for Frontiers in Neuroscience from 2016 - 2020
Year Value
2020 1.499
2019 1.554
2018 1.665
2017 1.769
2016 1.941
graph view Graph view
table view Table view

1.184

2% from 2019

SNIP for Frontiers in Neuroscience from 2016 - 2020
Year Value
2020 1.184
2019 1.211
2018 1.196
2017 1.183
2016 1.096
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 6% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 4% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 2% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Frontiers in Neuroscience

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Frontiers Media

Frontiers in Neuroscience

Approved by publishing and review experts on SciSpace, this template is built as per for Frontiers in Neuroscience formatting guidelines as mentioned in Frontiers Media author instructions. The current version was created on 10 Jun 2020 and has been used by 361 authors to write and format their manuscripts to this journal.

Neuroscience

i
Last updated on
10 Jun 2020
i
ISSN
1662-4548
i
Impact Factor
High - 1.078
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
frontiersinSCNS_ENG_HUMS
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Citation Type
Numbered
[25]
i
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/FNINS.2013.00267
MEG and EEG data analysis with MNE-Python

Abstract:

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. ... Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne. read more read less

Topics:

Python (programming language) (59%)59% related to the paper, NumPy (59%)59% related to the paper, Scripting language (53%)53% related to the paper, Code reuse (51%)51% related to the paper, Software suite (50%)50% related to the paper
View PDF
1,723 Citations
open accessOpen access Journal Article DOI: 10.3389/FNINS.2011.00073
Neuromorphic Silicon Neuron Circuits

Abstract:

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the applic... Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin-Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips. read more read less

Topics:

Neuromorphic engineering (62%)62% related to the paper
View PDF
1,559 Citations
open accessOpen access Journal Article DOI: 10.3389/FNINS.2010.00200
Modular and hierarchically modular organization of brain networks.
David Meunier1, Renaud Lambiotte2, Edward T. Bullmore1

Abstract:

Brain networks are increasingly understood as one of a large class of information processing systems that share important organizational principles in common, including the property of a modular community structure. A module is topologically defined as a subset of highly inter-connected nodes which are relatively sparsely con... Brain networks are increasingly understood as one of a large class of information processing systems that share important organizational principles in common, including the property of a modular community structure. A module is topologically defined as a subset of highly inter-connected nodes which are relatively sparsely connected to nodes in other modules. In brain networks, topological modules are often made up of anatomically neighboring and/or functionally related cortical regions, and inter-modular connections tend to be relatively long distance. Moreover, brain networks and many other complex systems demonstrate the property of hierarchical modularity, or modularity on several topological scales: within each module there will be a set of sub-modules, and within each sub-module a set of sub-sub-modules, etc. There are several general advantages to modular and hierarchically modular network organization, including greater robustness, adaptivity, and evolvability of network function. In this context, we review some of the mathematical concepts available for quantitative analysis of (hierarchical) modularity in brain networks and we summarize some of the recent work investigating modularity of structural and functional brain networks derived from analysis of human neuroimaging data. read more read less

Topics:

Modularity (networks) (70%)70% related to the paper, Modular design (58%)58% related to the paper
View PDF
1,042 Citations
open accessOpen access Journal Article DOI: 10.3389/FNINS.2012.00039
Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b.
Kai Keng Ang1, Zheng Yang Chin1, Chuanchu Wang1, Cuntai Guan1, Haihong Zhang1

Abstract:

The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specifi... The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10x10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively. read more read less

Topics:

Feature selection (52%)52% related to the paper, Feature (machine learning) (50%)50% related to the paper
View PDF
862 Citations
open accessOpen access Journal Article DOI: 10.3389/FNINS.2016.00508
Training Deep Spiking Neural Networks Using Backpropagation.
Jun Haeng Lee1, Tobi Delbruck1, Michael Pfeiffer1

Abstract:

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which tre... Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations. read more read less

Topics:

Spiking neural network (63%)63% related to the paper, Deep learning (61%)61% related to the paper, Artificial neural network (57%)57% related to the paper, Convolutional neural network (56%)56% related to the paper, MNIST database (56%)56% related to the paper
View PDF
818 Citations
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Frontiers in Neuroscience format uses frontiersinSCNS_ENG_HUMS citation style.

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Frequently asked questions

1. Can I write Frontiers in Neuroscience 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 Neuroscience guidelines and auto format it.

2. Do you follow the Frontiers in Neuroscience guidelines?

Yes, the template is compliant with the Frontiers in Neuroscience 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 Neuroscience?

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 Neuroscience citation style.

4. Can I use the Frontiers in Neuroscience 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 Neuroscience.

5. Can I use a manuscript in Frontiers in Neuroscience 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 Neuroscience that you can download at the end.

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

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 Neuroscience.

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

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 Neuroscience'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 Neuroscience'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 Neuroscience an online tool or is there a desktop version?

SciSpace's Frontiers in Neuroscience 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 Neuroscience?

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 Neuroscience?”

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

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

12. Is Frontiers in Neuroscience'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 Neuroscience?

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 Neuroscience. 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 Neuroscience?

The 5 most common citation types in order of usage for Frontiers in Neuroscience 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 Neuroscience?

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 Neuroscience's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Frontiers in Neuroscience 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 Neuroscience Endnote style according to Elsevier guidelines.

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