0899-7667
Published by: Massachusetts Institute of Technology Press
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 351 | 93 | 116 | 16 |
| Engineering and Technology | 961 | 15 | 19 | 8 |
Neural Computation primarily focuses on research topics in Artificial intelligence, Artificial neural network, Algorithm, Models of neural computation and Neuroscience. Artificial intelligence research featured in the journal incorporates concerns from various other topics such as Machine learning, Visual cortex, Computer vision and Pattern recognition. Some problems in Artificial neural network that were presented in it overlapped with concepts under Biological system and Nonlinear system.
The work on Algorithm tackled in Neural Computation brings together disciplines like Function (mathematics), Convergence (routing) and Mathematical optimization. The work on Mathematical optimization addressed in the journal expands to the thematically related Applied mathematics. Neuroscience studies presented include Neuron, Stimulus (physiology), Excitatory postsynaptic potential, Inhibitory postsynaptic potential and Synapse.
Artificial intelligence, Artificial neural network, Algorithm, Models of neural computation and Neuroscience are the main subjects of interest in the journal articles. While work presented in the most cited publications provide substantial information on Artificial intelligence, it also covers topics in Machine learning, Computer vision and Pattern recognition. The published papers tackle studies in Mathematical optimization and the interrelated subject of Applied mathematics to gain insights into Artificial neural network.
The journal covers a variety of subjects, including Artificial intelligence, Artificial neural network, Pattern recognition, Machine learning and Algorithm. The studies tackled, which mainly focus on Artificial intelligence, apply to Cognition as well. Aside from discussions in Artificial neural network, it also deals with the subject of Theoretical computer science which intersects with Representation (mathematics) disciplines.
The subject of Decoding methods, which is connected to the field of Sensory system and Electroencephalography, serves as the foundation of the Pattern recognition research featured in the journal. The studies on Machine learning discussed can also contribute to research in the domains of Anomaly detection, Computation and Regression. While work presented in it provided substantial information on Algorithm, it also covered topics in Basis (linear algebra), Fisher information, Kernel (statistics) and Curse of dimensionality.
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 Neural Computation (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 Neural Computation (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, 3.19% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 15.38% were posted by at least one author from the top 10 institutions publishing in the journal. Another 9.89% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.38% of all publications and 59.34% 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.
A career in neural computation opens up a range of professional opportunities across different sectors. Tapping into the potential of this breakthrough field not only enables professionals to have a profound impact on technological advancements, but also to secure well-paid roles in reputable institutions globally. For instance, professionals might consider pursuing a career as an academic researcher. This often begins with obtaining an undergraduate and postgraduate degree in disciplines such as computer science, mathematics, neuroscience, or related fields. Afterwards, subsequent steps typically involve pursuing relevant research projects, publishing in scholarly journals, and eventually obtaining advanced positions at universities or research institutes. Parallel to academia, numerous opportunities also exist in the private sector. Roles such as Data Scientists, Machine Learning Engineers, and AI Researchers are increasingly demanding professionals who are well-versed in neural computation principles, granting attractive remuneration packages and growth prospects. Additionally, for individuals with a passion for education, there is significant demand for qualified teachers who can prepare the next generation for a future that weaves in artificial intelligence seamlessly with the real world. For instance, if you are curious about the path towards becoming a teacher and wonder [how long does it take to become a teacher in Ohio], similar timelines might apply to the process of becoming a teacher in neural computation, given the prerequisites of completing a bachelor's degree, obtaining teacher certification, and acquiring teaching experience. To conclude, irrespective of the chosen career path, the benefits of delving into the realm of neural computation are manifold. Whether it's the allure of advanced research, the prospect of making a real-world impact, or the passion for nurturing future talent, opportunities exist across the spectrum for professionals armed with skills in neural computation.
Jing Gao;Peng Li;Zhikui Chen;Jianing Zhang
(2020)Tyler L. Hayes;Giri P. Krishnan;Maxim Bazhenov;Hava T. Siegelmann
(2021)Shagun Sodhani;Sarath Chandar;Yoshua Bengio
(2020)E Paxon Frady;Spencer J Kent;Bruno A Olshausen;Friedrich T Sommer
(2020)Zeke Xie;Fengxiang He;Shaopeng Fu;Issei Sato
(2021)Ilenna Simone Jones;Konrad Paul Kording
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