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Frontiers in Computational Neuroscience — Template for authors

Publisher: Frontiers Media

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Guideline source

Categories	Rank	Trend in last 3 yrs
Neuroscience (miscellaneous)	#8 of 24	up by 6 ranks
Cellular and Molecular Neuroscience	#59 of 88	up by 7 ranks

Journal quality:

Good

Last 4 years overview: 419 Published Papers | 1976 Citations

Indexed in: Scopus

Last updated: 19/06/2020

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General info

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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.

2.535

9% from 2018

Impact factor for Frontiers in Computational Neuroscience from 2016 - 2019
Year	Value
2019	2.535
2018	2.323
2017	2.073
2016	1.821

Graph view

4.7

2% from 2019

CiteRatio for Frontiers in Computational Neuroscience from 2016 - 2020
Year	Value
2020	4.7
2019	4.8
2018	4.5
2017	3.9
2016	4.6

Graph view

Insights

Impact factor of this journal has increased by 9% in last year.
This journal’s impact factor is in the top 10 percentile category.

Insights

CiteRatio of this journal has decreased by 2% 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.

0.794

24% from 2019

SJR for Frontiers in Computational Neuroscience from 2016 - 2020
Year	Value
2020	0.794
2019	1.041
2018	0.988
2017	1.08
2016	1.254

Graph view

1.154

19% from 2019

SNIP for Frontiers in Computational Neuroscience from 2016 - 2020
Year	Value
2020	1.154
2019	0.972
2018	1.01
2017	0.832
2016	0.789

Graph view

Insights

SJR of this journal has decreased by 24% in last years.
This journal’s SJR is in the top 10 percentile category.

Insights

SNIP of this journal has increased by 19% in last years.
This journal’s SNIP is in the top 10 percentile category.

Frontiers in Computational Neuroscience

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Frontiers Media

Frontiers in Computational Neuroscience

Frontiers in Computational Neuroscience is a Specialty Journal of Frontiers in Neuroscience. Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Read Less

Neuroscience

Last updated on	19 Jun 2020
ISSN	1662-5188
Impact Factor	Medium - 0.67
Open Access	No
Sherpa RoMEO Archiving Policy	Green
Plagiarism Check	Available via Turnitin
Endnote Style	Download Available
Bibliography Name	frontiersinSCNS_ENG_HUMS
Citation Type	Numbered [25]
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 access • Journal Article • DOI: 10.3389/FNCOM.2015.00099 •

Unsupervised learning of digit recognition using spike-timing-dependent plasticity.

Peter U. Diehl¹, Matthew Cook¹ •

03 Aug 2015 - Frontiers in Computational Neuroscience

Abstract:

In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in re... In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN) can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns), since most of such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e. conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks. read more

Topics:

Spiking neural network (61%)61% related to the paper, Unsupervised learning (57%)57% related to the paper, Artificial neural network (56%)56% related to the paper,

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1,098 Citations

Open access • Journal Article • DOI: 10.3389/FNCOM.2016.00094 •

Toward an Integration of Deep Learning and Neuroscience.

Adam H. Marblestone¹, Greg Wayne², •

14 Sep 2016 - Frontiers in Computational Neuroscience

Abstract:

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively... Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses. read more

Topics:

Artificial neural network (56%)56% related to the paper, Deep learning (55%)55% related to the paper, Reinforcement learning (54%)54% related to the paper,

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439 Citations

Open access • Journal Article • DOI: 10.3389/FNCOM.2013.00051 •

The neural origin of muscle synergies

Emilio Bizzi¹, Vincent C. K. Cheung² •

29 Apr 2013 - Frontiers in Computational Neuroscience

Abstract:

Muscle synergies are neural coordinative structures that function to alleviate the computational burden associated with the control of movement and posture. In this commentary, we address two critical questions: the explicit encoding of muscle synergies in the nervous system, and how muscle synergies simplify movement product... Muscle synergies are neural coordinative structures that function to alleviate the computational burden associated with the control of movement and posture. In this commentary, we address two critical questions: the explicit encoding of muscle synergies in the nervous system, and how muscle synergies simplify movement production. We argue that shared and task-specific muscle synergies are neurophysiological entities whose combination, orchestrated by the motor cortical areas and the afferent systems, facilitates motor control and motor learning. read more

Topics:

Motor control (57%)57% related to the paper, Motor learning (51%)51% related to the paper

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373 Citations

Open access • Journal Article • DOI: 10.3389/FNCOM.2017.00048 •

Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function

Michael W. Reimann¹, Max Nolte¹, •

12 Jun 2017 - Frontiers in Computational Neuroscience

Abstract:

The lack of a formal link between neural network structure and its emergent function has hampered our understanding of how the brain processes information. We have now come closer to describing such a link by taking the direction of synaptic transmission into account, constructing graphs of a network that reflect the directio... The lack of a formal link between neural network structure and its emergent function has hampered our understanding of how the brain processes information. We have now come closer to describing such a link by taking the direction of synaptic transmission into account, constructing graphs of a network that reflect the direction of information flow, and analyzing these directed graphs using algebraic topology. Applying this approach to a local network of neurons in the neocortex revealed a remarkably intricate and previously unseen topology of synaptic connectivity. The synaptic network contains an abundance of cliques of neurons bound into cavities that guide the emergence of correlated activity. In response to stimuli, correlated activity binds synaptically connected neurons into functional cliques and cavities that evolve in a stereotypical sequence towards peak complexity. We propose that the brain processes stimuli by forming increasingly complex functional cliques and cavities. read more

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364 Citations

Open access • Journal Article • DOI: 10.3389/FNCOM.2010.00146 •

Decision Making Under Uncertainty: A Neural Model Based on Partially Observable Markov Decision Processes

Rajesh P. N. Rao¹ •

24 Nov 2010 - Frontiers in Computational Neuroscience

Abstract:

A fundamental problem faced by animals is learning to select actions based on noisy sensory information and incomplete knowledge of the world. It has been suggested that the brain engages in Bayesian inference during perception but how such probabilistic representations are used to select actions has remained unclear. Here we... A fundamental problem faced by animals is learning to select actions based on noisy sensory information and incomplete knowledge of the world. It has been suggested that the brain engages in Bayesian inference during perception but how such probabilistic representations are used to select actions has remained unclear. Here we propose a neural model of action selection and decision making based on the theory of partially observable Markov decision processes (POMDPs). Actions are selected based not on a single “optimal” estimate of state but on the posterior distribution over states (the “belief” state). We show how such a model provides a unified framework for explaining experimental results in decision making that involve both information gathering and overt actions. The model utilizes temporal difference (TD) learning for maximizing expected reward. The resulting neural architecture posits an active role for the neocortex in belief computation while ascribing a role to the basal ganglia in belief representation, value computation, and action selection. When applied to the random dots motion discrimination task, model neurons representing belief exhibit responses similar to those of LIP neurons in primate neocortex. The appropriate threshold for switching from information gathering to overt actions emerges naturally during reward maximization. Additionally, the time course of reward prediction error in the model shares similarities with dopaminergic responses in the basal ganglia during the random dots task. For tasks with a deadline, the model learns a decision making strategy that changes with elapsed time, predicting a collapsing decision threshold consistent with some experimental studies. The model provides a new framework for understanding neural decision making and suggests an important role for interactions between the neocortex and the basal ganglia in learning the mapping between probabilistic sensory representations and actions that maximize rewards. read more

Topics:

Markov decision process (57%)57% related to the paper, Reinforcement learning (54%)54% related to the paper, Decision theory (54%)54% related to the paper,

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292 Citations

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

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 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:

Pre-prints as being the version of the paper before peer review and
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 Computational Neuroscience?

The 5 most common citation types in order of usage for Frontiers in Computational Neuroscience 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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Frontiers in Computational Neuroscience — Template for authors

Related Journals

Journal Performance & Insights

Impact Factor

CiteRatio

2.535

4.7

Insights

Insights

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

0.794

1.154

Insights

Insights

Frontiers in Computational Neuroscience

Frontiers in Computational Neuroscience

Top papers written in this journal

Abstract:

Topics:

Abstract:

Topics:

Abstract:

Topics:

Abstract:

Abstract:

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What to expect from SciSpace?

Speed and accuracy over MS Word

Plagiarism Reports via Turnitin

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Frequently asked questions

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No word template required

Verifed journal formats

Trusted by academicians

Fast and reliable,
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