Example of Journal of Computational Neuroscience format
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Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format
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Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format Example of Journal of Computational Neuroscience format
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open access Open Access

Journal of Computational Neuroscience — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Cognitive Neuroscience #55 of 96 down down by 6 ranks
Sensory Systems #26 of 40 down down by 6 ranks
Cellular and Molecular Neuroscience #77 of 88 down down by 7 ranks
journal-quality-icon Journal quality:
Medium
calendar-icon Last 4 years overview: 131 Published Papers | 429 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 19/07/2020
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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

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

1.811

15% from 2018

Impact factor for Journal of Computational Neuroscience from 2016 - 2019
Year Value
2019 1.811
2018 1.568
2017 1.606
2016 1.483
graph view Graph view
table view Table view

3.3

3% from 2019

CiteRatio for Journal of Computational Neuroscience from 2016 - 2020
Year Value
2020 3.3
2019 3.4
2018 3.3
2017 3.5
2016 3.6
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 15% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 3% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

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.

0.666

29% from 2019

SJR for Journal of Computational Neuroscience from 2016 - 2020
Year Value
2020 0.666
2019 0.937
2018 0.811
2017 0.888
2016 0.787
graph view Graph view
table view Table view

0.698

27% from 2019

SNIP for Journal of Computational Neuroscience from 2016 - 2020
Year Value
2020 0.698
2019 0.954
2018 0.721
2017 0.749
2016 0.605
graph view Graph view
table view Table view

insights Insights

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

insights Insights

  • SNIP of this journal has decreased by 27% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Journal of Computational Neuroscience

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Springer

Journal of Computational Neuroscience

The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences, as well as papers that fit the interface between theory and experiments. Primarily, theoretical papers should deal wit...... Read More

Neuroscience

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Last updated on
19 Jul 2020
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ISSN
0929-5313
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Impact Factor
High - 1.046
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Acceptance Rate
54%
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Open Access
Yes
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Sherpa RoMEO Archiving Policy
Green faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
SPBASIC
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Citation Type
Author Year
(Blonder et al, 1982)
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Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM (1982) Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys Rev B 25(7):4515–4532, URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

Journal Article DOI: 10.1023/A:1008925309027
Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.
Nicolas Brunel1

Abstract:

The dynamics of networks of sparsely connected excitatory and inhibitory integrate-and-fire neurons are studied analytically. The analysis reveals a rich repertoire of states, including synchronous states in which neurons fire regularly; asynchronous states with stationary global activity and very irregular individual cell ac... The dynamics of networks of sparsely connected excitatory and inhibitory integrate-and-fire neurons are studied analytically. The analysis reveals a rich repertoire of states, including synchronous states in which neurons fire regularly; asynchronous states with stationary global activity and very irregular individual cell activity; and states in which the global activity oscillates but individual cells fire irregularly, typically at rates lower than the global oscillation frequency. The network can switch between these states, provided the external frequency, or the balance between excitation and inhibition, is varied. Two types of network oscillations are observed. In the fast oscillatory state, the network frequency is almost fully controlled by the synaptic time scale. In the slow oscillatory state, the network frequency depends mostly on the membrane time constant. Finite size effects in the asynchronous state are also discussed. read more read less

Topics:

Synfire chain (56%)56% related to the paper
View PDF
1,758 Citations
open accessOpen access Journal Article DOI: 10.1007/S10827-007-0038-6
Simulation of networks of spiking neurons: A review of tools and strategies

Abstract:

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spi... We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks. read more read less

Topics:

Spiking neural network (59%)59% related to the paper
873 Citations
open accessOpen access Journal Article DOI: 10.1007/S10827-010-0262-3
Transfer entropy--a model-free measure of effective connectivity for the neurosciences
Raul Vicente1, Michael Wibral2, Michael Lindner2, Gordon Pipa1

Abstract:

Understanding causal relationships, or effective connectivity, between parts of the brain is of utmost importance because a large part of the brain's activity is thought to be internally generated and, hence, quantifying stimulus response relationships alone does not fully describe brain dynamics. Past efforts to determine ef... Understanding causal relationships, or effective connectivity, between parts of the brain is of utmost importance because a large part of the brain's activity is thought to be internally generated and, hence, quantifying stimulus response relationships alone does not fully describe brain dynamics. Past efforts to determine effective connectivity mostly relied on model based approaches such as Granger causality or dynamic causal modeling. Transfer entropy (TE) is an alternative measure of effective connectivity based on information theory. TE does not require a model of the interaction and is inherently non-linear. We investigated the applicability of TE as a metric in a test for effective connectivity to electrophysiological data based on simulations and magnetoencephalography (MEG) recordings in a simple motor task. In particular, we demonstrate that TE improved the detectability of effective connectivity for non-linear interactions, and for sensor level MEG signals where linear methods are hampered by signal-cross-talk due to volume conduction. read more read less

Topics:

Transfer entropy (56%)56% related to the paper
View PDF
831 Citations
Journal Article DOI: 10.1007/BF00962716
Synchronous oscillations in neuronal systems: mechanisms and functions.
Charles M. Gray1

Abstract:

How are the functions performed by one part of the nervous system integrated with those of another? This fundamental issue pervades virtually every aspect of brain function from sensory and cognitive processing to motor control. Yet from a physiological perspective we know very little about the neural mechanisms underlying th... How are the functions performed by one part of the nervous system integrated with those of another? This fundamental issue pervades virtually every aspect of brain function from sensory and cognitive processing to motor control. Yet from a physiological perspective we know very little about the neural mechanisms underlying the integration of distributed processes in the nervous system. Even the simplest of sensori-motor acts engages vast numbers of cells in many different parts of the brain. Such actions require coordination between a host of neural systems, each of which must carry out parallel computations involving large populations of interconnected neurons. It seems reasonable to assume that a mechanism or class of mechanisms has evolved to temporally coordinate the activity within and between subsystems of the central nervous system. For several reasons, neuronal rhythms have long been thought to play an important role in such coordination. Since the discovery of the Electroencephalogram (EEG) over 60 years ago it has been known that a number of structures in the mammalian brain engage in rhythmic activities. These patterned neuronal oscillations take many forms. They occur over a broad range of frequencies, and are present in a multitude of different systems in the b~ain, during a variety of different behavioral states. They are often the most salient aspect of observable electrical activity in the brain and typically encompass widespread regions of cerebral tissue. With the advent of new techniques of multielectrode recording and neural imaging, it is now within the realm of possibility 'to record from 100 single neurons simultaneously (Wilson and McNaughton, 1993), to optically measure the activity in a cortical area (Blasdel and Salama, 1986; T'so et al., 1990), or to noninvasively image the pattern of electric current flow in an alert human being performing a task (Pantev et al., 1991). These new techniques have revealed that spatially and temporally organized activity among distributed populations of cells often takes the form of synchronous rhythms. When combined with cellular neurophysiological and anatomical studies these findings provide new insights into the behavior and mechanisms controUing the coordination of activity in neuronal populations. read more read less

Topics:

Cortical Synchronization (57%)57% related to the paper
771 Citations
Journal Article DOI: 10.1007/BF00961879
When inhibition not excitation synchronizes neural firing.
Carl van Vreeswijk1, Larry F. Abbott1, G. Bard Ermentrout2

Abstract:

Excitatory and inhibitory synaptic coupling can have counter-intuitive effects on the synchronization of neuronal firing. While it might appear that excitatory coupling would lead to synchronization, we show that frequently inhibition rather than excitation synchronizes firing. We study two identical neurons described by inte... Excitatory and inhibitory synaptic coupling can have counter-intuitive effects on the synchronization of neuronal firing. While it might appear that excitatory coupling would lead to synchronization, we show that frequently inhibition rather than excitation synchronizes firing. We study two identical neurons described by integrate-and-fire models, general phase-coupled models or the Hodgkin-Huxley model with mutual, non-instantaneous excitatory or inhibitory synapses between them. We find that if the rise time of the synapse is longer than the duration of an action potential, inhibition not excitation leads to synchronized firing. read more read less

Topics:

Excitatory synapse (60%)60% related to the paper, Inhibitory postsynaptic potential (53%)53% related to the paper, Excitatory postsynaptic potential (53%)53% related to the paper
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750 Citations
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Journal of Computational Neuroscience format uses SPBASIC citation style.

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Frequently asked questions

1. Can I write Journal of Computational 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 Journal of Computational Neuroscience guidelines and auto format it.

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Yes, the template is compliant with the Journal of Computational 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 Journal of Computational 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 Journal of Computational Neuroscience citation style.

4. Can I use the Journal of Computational 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 Journal of Computational Neuroscience.

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

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7. Where can I find the template for the Journal of Computational Neuroscience?

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SciSpace's Journal of Computational 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.

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12. Is Journal of Computational 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 Journal of Computational 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 Journal of Computational 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 Journal of Computational Neuroscience?

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

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16. Can I download Journal of Computational 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 Journal of Computational Neuroscience Endnote style according to Elsevier guidelines.

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