Frontiers in Computational Neuroscience Journal Impact Factor & Information

Publisher: Frontiers Research Foundation, Frontiers

Current impact factor: 2.20

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2014 Impact Factor 2.201
2013 Impact Factor 2.233
2012 Impact Factor 2.481
2011 Impact Factor 2.147
2010 Impact Factor 2.586

Impact factor over time

Impact factor

Additional details

5-year impact 2.50
Cited half-life 2.40
Immediacy index 0.42
Eigenfactor 0.01
Article influence 0.90
ISSN 1662-5188
OCLC 250614660
Material type Document, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details


  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • On open access repositories
    • Authors retain copyright
    • Creative Commons Attribution License
    • Published source must be acknowledged
    • Publisher's version/PDF may be used
    • Set statement to accompany [This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.]
    • Articles are placed in PubMed Central immediately on behalf of authors.
    • All titles are open access journals
    • Publisher last contacted on 16/07/2015
  • Classification

Publications in this journal

  • Frontiers in Computational Neuroscience 11/2015; 9(80). DOI:10.3389/fncom.2015.00142
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    ABSTRACT: Voltage gated ion channels play a major role in determining a neuron's firing behavior, resulting in the specific processing of synaptic input patterns. Drosophila and other invertebrates provide valuable model systems for investigating ion channel kinetics and their impact on firing properties. Despite the increasing importance of Drosophila as a model system, few computational models of its ion channel kinetics have been developed. In this study, experimentally observed biophysical properties of voltage gated ion channels from the fruitfly Drosophila melanogaster are used to develop a minimal, conductance based neuron model. We investigate the impact of the densities of these channels on the excitability of the model neuron. Changing the channel densities reproduces different in situ observed firing patterns and induces a switch from integrator to resonator properties. Further, we analyze the preference to input frequency and how it depends on the channel densities and the resulting bifurcation type the system undergoes. An extension to a three dimensional model demonstrates that the inactivation kinetics of the sodium channels play an important role, allowing for firing patterns with a delayed first spike and subsequent high frequency firing as often observed in invertebrates, without altering the kinetics of the delayed rectifier current.
    Frontiers in Computational Neuroscience 11/2015; 9. DOI:10.3389/fncom.2015.00139
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    ABSTRACT: How networks endure damage is a central issue in neural network research. In this paper, we study the slow and fast dynamics of network damage and compare the results for two simple but very different models of recurrent and feed forward neural network. What we find is that a slower degree of network damage leads to a better chance of recovery in both types of network architecture. This is in accord with many experimental findings on the damage inflicted by strokes and by slowly growing tumors. Here, based on simulation results, we explain the seemingly paradoxical observation that disability caused by lesions, affecting large portions of tissue, may be less severe than the disability caused by smaller lesions, depending on the speed of lesion growth.
    Frontiers in Computational Neuroscience 11/2015; 9:130. DOI:10.3389/fncom.2015.00130
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    ABSTRACT: To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in “compatible pairs” connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions.
    Frontiers in Computational Neuroscience 11/2015; 9. DOI:10.3389/fncom.2015.00118
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    ABSTRACT: Perception of natural visual scenes activates several functional areas in the human brain, including the Parahippocampal Place Area (PPA), Retrosplenial Complex (RSC), and the Occipital Place Area (OPA). It is currently unclear what specific scene-related features are represented in these areas. Previous studies have suggested that PPA, RSC, and/or OPA might represent at least three qualitatively different classes of features: (1) 2D features related to Fourier power; (2) 3D spatial features such as the distance to objects in a scene; or (3) abstract features such as the categories of objects in a scene. To determine which of these hypotheses best describes the visual representation in scene-selective areas, we applied voxel-wise modeling (VM) to BOLD fMRI responses elicited by a set of 1386 images of natural scenes. VM provides an efficient method for testing competing hypotheses by comparing predictions of brain activity based on encoding models that instantiate each hypothesis. Here we evaluated three different encoding models that instantiate each of the three hypotheses listed above. We used linear regression to fit each encoding model to the fMRI data recorded from each voxel, and we evaluated each fit model by estimating the amount of variance it predicted in a withheld portion of the data set. We found that voxel-wise models based on Fourier power or the subjective distance to objects in each scene predicted much of the variance predicted by a model based on object categories. Furthermore, the response variance explained by these three models is largely shared, and the individual models explain little unique variance in responses. Based on an evaluation of previous studies and the data we present here, we conclude that there is currently no good basis to favor any one of the three alternative hypotheses about visual representation in scene-selective areas. We offer suggestions for further studies that may help resolve this issue.
    Frontiers in Computational Neuroscience 11/2015; 9(23). DOI:10.3389/fncom.2015.00135
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    ABSTRACT: Differentiating between Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) is still a challenge, specially at early stages when the patients show similar symptoms. During last years, several computer systems have been proposed in order to improve the diagnosis of PD, but their accuracy is still limited. In this work we demonstrate a full automatic computer system to assist the diagnosis of PD using 18F-DMFP PET data. First, a few regions of interest are selected by means of a two-sample t-test. The accuracy of the selected regions to separate PD from APS patients is then computed using a support vector machine classifier. The accuracy values are finally used to train a Bayesian network that can be used to predict the class of new unseen data. This methodology was evaluated using a database with 87 neuroimages, achieving accuracy rates over 78%. A fair comparison with other similar approaches is also provided.
    Frontiers in Computational Neuroscience 11/2015; 9(156). DOI:10.3389/fncom.2015.00137
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    ABSTRACT: Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties.
    Frontiers in Computational Neuroscience 11/2015; 9. DOI:10.3389/fncom.2015.00134
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    ABSTRACT: A plasma stratum (cell free layer or CFL) generated by flowing blood interposed between the red blood cell (RBC) core and the endothelium affects generation, consumption, and transport of nitric oxide (NO) in the microcirculation. CFL width is a principal factor modulating NO diffusion and vessel wall shears stress development, thus significantly affecting NO bioavailability. Since the CFL is bounded by the surface formed by the chaotically moving RBCs and the stationary but spatially non-uniform endothelial surface, its width fluctuates randomly in time and space. We analyze how these stochastic fluctuations affect NO transport in the CFL and NO bioavailability. We show that effects due to random boundaries do not average to zero and lead to an increase of NO bioavailability. Since endothelial production of NO is significantly enhanced by temporal variability of wall shear stress, we posit that stochastic shear stress stimulation of the endothelium yields the baseline continual production of NO by the endothelium. The proposed stochastic formulation captures the natural continuous and microscopic variability, whose amplitude is measurable and is of the scale of cellular dimensions. It provides a realistic model of NO generation and regulation.
    Frontiers in Computational Neuroscience 10/2015; 9. DOI:10.3389/fncom.2015.00131
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    ABSTRACT: The subject of the effects of the active properties of the Purkinje cell dendrite on neuronal function has been an active subject of study for more than 40 years. Somewhat unusually, some of these investigations, from the outset have involved an interacting combination of experimental and model-based techniques. This article recounts that 40-year history, and the view of the functional significance of the active properties of the Purkinje cell dendrite that has emerged. It specifically considers the emergence from these efforts of what is arguably the first single cell "community" model in neuroscience. The article also considers the implications of the development of this model for future studies of the complex properties of neuronal dendrites.
    Frontiers in Computational Neuroscience 10/2015; 9. DOI:10.3389/fncom.2015.00129
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    ABSTRACT: How is the brain configured for creativity? What is the computational substrate for ‘eureka’ moments of insight? Here we argue that creative thinking arises ultimately from a synergy between low-energy stochastic and energy-intensive deterministic processing, and is a by-product of a nervous system whose signal-processing capability per unit of available energy has become highly energy optimised. We suggest that the stochastic component has its origin in thermal (ultimately quantum decoherent) noise affecting the activity of neurons. Without this component, deterministic computational models of the brain are incomplete.
    Frontiers in Computational Neuroscience 10/2015; 9. DOI:10.3389/fncom.2015.00124
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    ABSTRACT: Mounting evidence suggests that both α and γ motoneurons are active during movement and posture, but how does the central motor system coordinate the α-γ controls in these tasks remains sketchy due to lack of in vivo data. Here a computational model of α-γ control of muscles and spindles was used to investigate α-γ integration and coordination for movement and posture. The model comprised physiologically realistic spinal circuitry, muscles, proprioceptors, and skeletal biomechanics. In the model, we divided the cortical descending commands into static and dynamic sets, where static commands (α s and γ s ) were for posture maintenance and dynamic commands (α d and γ d ) were responsible for movement. We matched our model to human reaching movement data by straightforward adjustments of descending commands derived from either minimal-jerk trajectories or human EMGs. The matched movement showed smooth reach-to-hold trajectories qualitatively close to human behaviors, and the reproduced EMGs showed the classic tri-phasic patterns. In particular, the function of γ d was to gate the α d command at the propriospinal neurons (PN) such that antagonistic muscles can accelerate or decelerate the limb with proper timing. Independent control of joint position and stiffness could be achieved by adjusting static commands. Deefferentation in the model indicated that accurate static commands of α s and γ s are essential to achieve stable terminal posture precisely, and that the γ d command is as important as the α d command in controlling antagonistic muscles for desired movements. Deafferentation in the model showed that losing proprioceptive afferents mainly affected the terminal position of movement, similar to the abnormal behaviors observed in human and animals. Our results illustrated that tuning the simple forms of α-γ commands can reproduce a range of human reach-to-hold movements, and it is necessary to coordinate the set of α-γ descending commands for accurate and stable control of movement and posture.
    Frontiers in Computational Neuroscience 10/2015; 9. DOI:10.3389/fncom.2015.00122