Frontiers in Computational Neuroscience Q3
Unclaimed
Frontiers in Computational Neuroscience is a journal indexed in SJR in Neuroscience (miscellaneous) and Cellular and Molecular Neuroscience with an H index of 71. It is an CC BY Journal with a Single blind Peer Review review system, and It has a price of 2655 €. The scope of the journal is focused on theoretical neuroscience. It has an SJR impact factor of 0,73 and it has a best quartile of Q3. It is published in English. It has an SJR impact factor of 0,73.
Frontiers in Computational Neuroscience focuses its scope in these topics and keywords: small, inferior, improve, identificationunderstanding, grid, global, glass, gapjunctional, frequency, inhibitory, ...
Type: Journal
Type of Copyright: CC BY
Languages: English
Open Access Policy: Open Access
Type of publications:
Publication frecuency: -
2655 €
Inmediate OANPD
Embargoed OA- €
Non OAMetrics
0,73
SJR Impact factor71
H Index144
Total Docs (Last Year)443
Total Docs (3 years)7938
Total Refs1254
Total Cites (3 years)428
Citable Docs (3 years)2.39
Cites/Doc (2 years)55.13
Ref/DocOther journals with similar parameters
IBRO Reports Q3
Journal of Mathematical Neuroscience Q3
Neuroscience Letters Q3
AIMS Neuroscience Q3
BMC Neuroscience Q3
Compare this journals
Aims and Scope
Best articles by citations
Unsupervised learning of digit recognition using spike-timing-dependent plasticity
Toward an Integration of Deep Learning and Neuroscience
Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function
The energy cost of action potential propagation in dopamine neurons: clues to susceptibility in Parkinson's disease
Data Augmentation for Brain-Tumor Segmentation: A Review
Common muscle synergies for balance and walking
Motor thalamus integration of cortical, cerebellar and basal ganglia information: implications for normal and parkinsonian conditions
Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation
Physical principles for scalable neural recording
Editorial: Artificial Neural Networks as Models of Neural Information Processing
Neural masses and fields in dynamic causal modeling
The number and choice of muscles impact the results of muscle synergy analyses
Large-scale network organization in the avian forebrain: a connectivity matrix and theoretical analysis
Cross-frequency coupling in real and virtual brain networks
Neural bases of hand synergies
Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning
Automatic Brain Tumor Segmentation Based on Cascaded Convolutional Neural Networks With Uncertainty Estimation
Defining nodes in complex brain networks
Coexisting Behaviors of Asymmetric Attractors in Hyperbolic-Type Memristor based Hopfield Neural Network
Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features
Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review
Comments