Example of Cognitive Neurodynamics format
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Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format
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Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format Example of Cognitive Neurodynamics format
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open access Open Access

Cognitive Neurodynamics — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Cognitive Neuroscience #19 of 96 up up by 28 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 200 Published Papers | 1322 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 08/06/2020
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Quality:  
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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.

3.925

30% from 2018

Impact factor for Cognitive Neurodynamics from 2016 - 2019
Year Value
2019 3.925
2018 3.021
2017 2.0
2016 1.828
graph view Graph view
table view Table view

6.6

18% from 2019

CiteRatio for Cognitive Neurodynamics from 2016 - 2020
Year Value
2020 6.6
2019 5.6
2018 4.6
2017 3.9
2016 3.4
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 30% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 18% 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.

0.83

20% from 2019

SJR for Cognitive Neurodynamics from 2016 - 2020
Year Value
2020 0.83
2019 1.044
2018 0.982
2017 0.802
2016 0.591
graph view Graph view
table view Table view

1.346

31% from 2019

SNIP for Cognitive Neurodynamics from 2016 - 2020
Year Value
2020 1.346
2019 1.027
2018 1.149
2017 0.902
2016 0.857
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 20% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 31% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Cognitive Neurodynamics

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Springer

Cognitive Neurodynamics

The aim of Cognitive Neurodynamics is to provide a unique dynamic forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics and associated science, that is, to bridge the gap between theory including numerical simulatio...... Read More

Neuroscience

i
Last updated on
08 Jun 2020
i
ISSN
1871-4080
i
Impact Factor
Medium - 0.853
i
Acceptance Rate
60%
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
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
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1007/S11571-013-9277-6
Exponential synchronization of memristive Cohen–Grossberg neural networks with mixed delays
Xinsong Yang1, Jinde Cao2, Jinde Cao3, Wenwu Yu3, Wenwu Yu4
04 Jan 2014 - Cognitive Neurodynamics

Abstract:

This paper concerns the problem of global exponential synchronization for a class of memristor-based Cohen–Grossberg neural networks with time-varying discrete delays and unbounded distributed delays. The drive-response set is discussed. A novel controller is designed such that the response (slave) system can be controlled to... This paper concerns the problem of global exponential synchronization for a class of memristor-based Cohen–Grossberg neural networks with time-varying discrete delays and unbounded distributed delays. The drive-response set is discussed. A novel controller is designed such that the response (slave) system can be controlled to synchronize with the drive (master) system. Through a nonlinear transformation, we get an alternative system from the considered memristor-based Cohen–Grossberg neural networks. By investigating the global exponential synchronization of the alternative system, we obtain the corresponding synchronization criteria of the considered memristor-based Cohen–Grossberg neural networks. Moreover, the conditions established in this paper are easy to be verified and improve the conditions derived in most of existing papers concerning stability and synchronization for memristor-based neural networks. Numerical simulations are given to show the effectiveness of the theoretical results. read more read less

Topics:

Memristor (54%)54% related to the paper, Synchronization (52%)52% related to the paper, Artificial neural network (51%)51% related to the paper
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170 Citations
open accessOpen access Journal Article DOI: 10.1007/S11571-008-9064-Y
Simulated power spectral density (PSD) of background electrocorticogram (ECoG)
Walter J. Freeman1, Jian Zhai2
01 Jan 2009 - Cognitive Neurodynamics

Abstract:

The ECoG background activity of cerebral cortex in states of rest and slow wave sleep resembles broadband noise. The power spectral density (PSD) then may often conform to a power-law distribution: a straight line in coordinates of log power vs. log frequency. The exponent, x, of the distribution, 1/fx, ranges between 2 and 4... The ECoG background activity of cerebral cortex in states of rest and slow wave sleep resembles broadband noise. The power spectral density (PSD) then may often conform to a power-law distribution: a straight line in coordinates of log power vs. log frequency. The exponent, x, of the distribution, 1/fx, ranges between 2 and 4. These findings are explained with a model of the neural source of the background activity in mutual excitation among pyramidal cells. The dendritic response of a population of interactive excitatory neurons to an impulse input is a rapid exponential rise and a slow exponential decay, which can be fitted with the sum of two exponential terms. When that function is convolved as the kernel with pulses from a Poisson process and summed, the resulting “brown” or “black noise conforms to the ECoG time series and the PSD in rest and sleep. The PSD slope is dependent on the rate of rise. The variation in the observed slope is attributed to variation in the level of the background activity that is homeostatically regulated by the refractory periods of the excitatory neurons. Departures in behavior from rest and sleep to action are accompanied by local peaks in the PSD, which manifest emergent nonrandom structure in the ECoG, and which prevent reliable estimation of the 1/fx exponents in active states. We conclude that the resting ECoG truly is low-dimensional noise, and that the resting state is an optimal starting point for defining and measuring both artifactual and physiological structures emergent in the activated ECoG. read more read less

Topics:

Population (51%)51% related to the paper
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165 Citations
open accessOpen access Journal Article DOI: 10.1007/S11571-009-9087-Z
Distributed processing and temporal codes in neuronal networks.
Wolf Singer1
28 Jun 2009 - Cognitive Neurodynamics

Abstract:

The cerebral cortex presents itself as a distributed dynamical system with the characteristics of a small world network. The neuronal correlates of cognitive and executive processes often appear to consist of the coordinated activity of large assemblies of widely distributed neurons. These features require mechanisms for the ... The cerebral cortex presents itself as a distributed dynamical system with the characteristics of a small world network. The neuronal correlates of cognitive and executive processes often appear to consist of the coordinated activity of large assemblies of widely distributed neurons. These features require mechanisms for the selective routing of signals across densely interconnected networks, the flexible and context dependent binding of neuronal groups into functionally coherent assemblies and the task and attention dependent integration of subsystems. In order to implement these mechanisms, it is proposed that neuronal responses should convey two orthogonal messages in parallel. They should indicate (1) the presence of the feature to which they are tuned and (2) with which other neurons (specific target cells or members of a coherent assembly) they are communicating. The first message is encoded in the discharge frequency of the neurons (rate code) and it is proposed that the second message is contained in the precise timing relationships between individual spikes of distributed neurons (temporal code). It is further proposed that these precise timing relations are established either by the timing of external events (stimulus locking) or by internal timing mechanisms. The latter are assumed to consist of an oscillatory modulation of neuronal responses in different frequency bands that cover a broad frequency range from 40 Hz (gamma) and ripples. These oscillations limit the communication of cells to short temporal windows whereby the duration of these windows decreases with oscillation frequency. Thus, by varying the phase relationship between oscillating groups, networks of functionally cooperating neurons can be flexibly configurated within hard wired networks. Moreover, by synchronizing the spikes emitted by neuronal populations, the saliency of their responses can be enhanced due to the coincidence sensitivity of receiving neurons in very much the same way as can be achieved by increasing the discharge rate. Experimental evidence will be reviewed in support of the coexistence of rate and temporal codes. Evidence will also be provided that disturbances of temporal coding mechanisms are likely to be one of the pathophysiological mechanisms in schizophrenia. read more read less
View PDF
155 Citations
open accessOpen access Journal Article DOI: 10.1007/S11571-008-9038-0
Dynamic causal modelling for EEG and MEG
Stefan J. Kiebel1, Marta I. Garrido1, Rosalyn J. Moran1, Karl J. Friston1
23 Apr 2008 - Cognitive Neurodynamics

Abstract:

Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnetic resonance imaging (fMRI) to quantify effective connectivity between brain areas. Recently, this framework has been extended and established in the magneto/encephalography (M/EEG) domain. DCM for M/EEG entails the inversion a... Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnetic resonance imaging (fMRI) to quantify effective connectivity between brain areas. Recently, this framework has been extended and established in the magneto/encephalography (M/EEG) domain. DCM for M/EEG entails the inversion a full spatiotemporal model of evoked responses, over multiple conditions. This model rests on a biophysical and neurobiological generative model for electrophysiological data. A generative model is a prescription of how data are generated. The inversion of a DCM provides conditional densities on the model parameters and, indeed on the model itself. These densities enable one to answer key questions about the underlying system. A DCM comprises two parts; one part describes the dynamics within and among neuronal sources, and the second describes how source dynamics generate data in the sensors, using the lead-field. The parameters of this spatiotemporal model are estimated using a single (iterative) Bayesian procedure. In this paper, we will motivate and describe the current DCM framework. Two examples show how the approach can be applied to M/EEG experiments. read more read less

Topics:

Dynamic causal modelling (57%)57% related to the paper, Generative model (54%)54% related to the paper
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155 Citations
open accessOpen access Journal Article DOI: 10.1007/S11571-008-9044-2
Cortical network dynamics with time delays reveals functional connectivity in the resting brain.
Anandamohan Ghosh1, Young-Ah Rho2, Anthony R. McIntosh3, Rolf Kötter4, Viktor K. Jirsa1, Viktor K. Jirsa2
23 Apr 2008 - Cognitive Neurodynamics

Abstract:

In absence of all goal-directed behavior, a characteristic network of cortical regions involving prefrontal and cingulate cortices consistently shows temporally coherent fluctuations. The origin of these fluctuations is unknown, but has been hypothesized to be of stochastic nature. In the present paper we test the hypothesis ... In absence of all goal-directed behavior, a characteristic network of cortical regions involving prefrontal and cingulate cortices consistently shows temporally coherent fluctuations. The origin of these fluctuations is unknown, but has been hypothesized to be of stochastic nature. In the present paper we test the hypothesis that time delays in the network dynamics play a crucial role in the generation of these fluctuations. By tuning the propagation velocity in a network based on primate connectivity, we scale the time delays and demonstrate the emergence of the resting state networks for biophysically realistic parameters. read more read less

Topics:

Resting state fMRI (57%)57% related to the paper, Default mode network (56%)56% related to the paper, Network dynamics (52%)52% related to the paper
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138 Citations
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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 Cognitive Neurodynamics citation style.

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12. Is Cognitive Neurodynamics'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 Cognitive Neurodynamics?

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 Cognitive Neurodynamics. 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 Cognitive Neurodynamics?

The 5 most common citation types in order of usage for Cognitive Neurodynamics are:.

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

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