1662-5188
Published by: Frontiers Media S.A.
https://www.frontiersin.org/journals/computational-neuroscience
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Neuroscience | 164 | 100 | 114 | 18 |
Neuroscience, Artificial intelligence, Pattern recognition, Artificial neural network and Machine learning are among the topics commonly tackled in the journal. Neuroscience research discussed connects with the study of Synaptic plasticity. It holds forums on Artificial intelligence that merges themes from other disciplines such as Perception and Computer vision.
The most cited articles focus largely on the fields of Neuroscience, Artificial intelligence, Artificial neural network, Machine learning and Synaptic plasticity. Issues in Artificial intelligence were discussed in the published articles, taking into consideration concepts from other disciplines like Perception, Motor control and Pattern recognition. The published papers hold forums on Synaptic plasticity that merge themes from other disciplines such as Long-term potentiation and Neurotransmission.
The objective of the journal is to combine knowledge in the areas of Artificial intelligence, Artificial neural network, Pattern recognition, Neuroscience and Deep learning. The Artificial intelligence works featured in Frontiers in Computational Neuroscience incorporate elements from Machine learning and Identification (information). The research on Artificial neural network featured in the journal combines topics in other fields like Computer architecture, Memristor, Inference, Cognitive science and Random graph.
The subject of Learning rule, which is connected to the field of Unsupervised learning, serves as the foundation of the Pattern recognition research featured in it. The work on Neuroscience addressed in Frontiers in Computational Neuroscience expands to the thematically related Rhythm. While Deep learning is the focus of it, it also provided insights into the studies of Neuroimaging and Dyslexia.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Frontiers in Computational Neuroscience (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Frontiers in Computational Neuroscience (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 2.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 16.33% were posted by at least one author from the top 10 institutions publishing in the journal. Another 8.16% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 14.29% of all publications and 61.22% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
In the sphere of Computational Neuroscience, career opportunities are varied and plentiful. Aspiring scientists can pursue rewarding careers as research scientists, professors, data analysts, and more. But it's critical to understand that each role requires specific academic qualifications and licensure. One such career path is that of a Speech-Language Pathologist, who can play a vital role in assisting those with communication or swallowing disorders.
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Ultimately, a move into the field of Computational Neuroscience can be rewarding both professionally and personally. It's a sector ripe with opportunities for those with the willingness to obtain the necessary qualifications and continue in the pursuit of further knowledge and understanding.
Ryan Smith;Philipp Schwartenbeck;Thomas Parr;Karl J. Friston
(2020)Dennis Joe Harmah;Cunbo Li;Fali Li;Yuanyuan Liao
(2020)Daphne Demekas;Thomas Parr;Karl J. Friston
(2020)Ulises Pereira;Nicolas Brunel
(2020)Toviah Moldwin;Idan Segev
(2020)Idan Tal;Idan Tal;Samuel Neymotin;Stephan Bickel;Stephan Bickel;Peter Lakatos;Peter Lakatos
(2020)Edmund T Rolls;Edmund T Rolls
(2021)Marios Antonakakis;Stavros I. Dimitriadis;Michalis E. Zervakis;Andrew C. Papanicolaou
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