Write in a clean editor, then format for Frontiers in Computational Neuroscience in one click — DocuGuru applies the official Frontiers template with author–year references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
Frontiers in Computational Neuroscience is a peer-reviewed journal published by Frontiers, covering Neural dynamics and brain function, EEG and Brain-Computer Interfaces, Advanced Memory and Neural Computing. Over its lifetime it has published 3,168 papers, which have received 58,560 citations.
| Publisher | Frontiers |
|---|---|
| Reference style | Author–year (Harvard) Author–year — (Smith, 2023) in the text Smith, A., Jones, B. and Lee, C. (2023) 'A representative article title', Frontiers in Computational Neuroscience, 12(3), pp. 45–58.
Formats any DOI in Frontiers in Computational Neuroscience style. No sign-up. |
| Publishes research in | Neural dynamics and brain function EEG and Brain-Computer Interfaces Advanced Memory and Neural Computing Functional Brain Connectivity Studies Neuroscience and Neural Engineering |
| ISSN | 1662-5188 |
| Citation impact (2-yr) | 4.09 |
| h-index | 107 |
| i10-index | 1,229 |
| Article processing charge | $2,950 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | www.frontiersin.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
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…
We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like…
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,…
Dopamine neurons of the substantia nigra pars compacta (SNc) are uniquely sensitive to degeneration in Parkinson's disease (PD) and its models. Although a variety of molecular characteristics have been proposed to underlie this sensitivity, one possible contributory factor is their massive, unmyelinated axonal arbor that is orders of magnitude larger than other neuronal types. We…
Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal role in scenarios in which the amount of high-quality ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a very common problem in medical…
