Journal of Computational Neuroscience

Top Research Topics at Journal of Computational Neuroscience?

Neuroscience, Artificial intelligence, Theory of computation, Biological system and Excitatory postsynaptic potential are among the topics commonly tackled in Journal of Computational Neuroscience. Neuron, Inhibitory postsynaptic potential, Stimulus (physiology), Bursting and Electrophysiology are all aspects of Neuroscience discussed in Journal of Computational Neuroscience. Some problems in Stimulus (physiology) that were presented in the journal overlapped with concepts under Sensory system and Neural coding.

The work on Bursting addressed in Journal of Computational Neuroscience expands to the thematically related Theta model. Journal of Computational Neuroscience addresses concerns in the field of Artificial intelligence by exploring it in line with topics in Visual cortex which intersect with Receptive field subjects. Theory of computation research presented falls under the umbrella topic of Algorithm.

Topics in Biological system explored in it were investigated in conjunction with research in Simulation and Nonlinear system. Nonlinear system studies covered in the journal falls within the purview of Control theory. Journal of Computational Neuroscience explores research in Excitatory postsynaptic potential and the adjacent study of Neurotransmission.

• Neuroscience (45.69%)
• Artificial intelligence (20.19%)
• Theory of computation (16.39%)

What are the most cited papers published in the journal?

• Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. (1381 citations)
• Simulation of networks of spiking neurons: A review of tools and strategies (688 citations)
• Synchronous oscillations in neuronal systems: mechanisms and functions. (683 citations)

Research areas of the most cited articles at Journal of Computational Neuroscience:

The most cited articles focus largely on the fields of Neuroscience, Artificial intelligence, Theory of computation, Stimulus (physiology) and Biological system. The journal papers explore issues in Artificial intelligence which can be linked to other research areas like Information theory, Machine learning, Computer vision and Pattern recognition. In addition to Theory of computation research, the most cited articles aim to explore topics under Theoretical computer science and Topology.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Statistics
• Neuron

The previous edition focused in particular on these issues:

Journal of Computational Neuroscience investigates studies in Neuroscience, Theory of computation, Artificial intelligence, Saccade and Biological system. The journal links adjacent topics like Neuroscience with Synaptic plasticity. The journal focuses on Theory of computation but the discussions also offer insight into other areas such as Mathematics education, Ionic Channels and Line (text file).

The studies on Artificial intelligence discussed can also contribute to research in the domains of Container (abstract data type), Computer vision and Pattern recognition. Topics in Saccade were tackled in line with various other fields like Superior colliculus and Brainstem. While it focused on Biological system, it was also able to explore topics like Feature (machine learning), Spike train, Dynamics (mechanics), Synchronization and Spatial network.

The most cited articles from the last journal are:

• Frontal eye field inactivation alters the readout of superior colliculus activity for saccade generation in a task-dependent manner. (4 citations)
• Monosynaptic inference via finely-timed spikes. (3 citations)
• Predictive coding models for pain perception. (2 citations)

Papers citation over time

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.

Top authors and change over time

The top authors publishing in Journal of Computational Neuroscience (based on the number of publications) are:

• G. Bard Ermentrout (19 papers) published 1 paper at the last edition,
• Alain Destexhe (16 papers) published 2 papers at the last edition,
• Michele Migliore (12 papers) absent at the last edition,
• Nancy Kopell (12 papers) absent at the last edition,
• Liam Paninski (12 papers) absent at the last edition.

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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Journal of Computational Neuroscience (based on the number of publications) are:

• University of Pittsburgh (49 papers) published 1 paper at the last edition,
• Max Planck Society (35 papers) absent at the last edition,
• Boston University (33 papers) absent at the last edition,
• Centre national de la recherche scientifique (32 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Courant Institute of Mathematical Sciences (24 papers) published 1 paper at the last edition the same number as at the previous edition.

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.

Publication chance based on affiliation

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, 3.70% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 17.31% were posted by at least one author from the top 10 institutions publishing in the journal. Another 9.62% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 9.62% of all publications and 63.46% were from other institutions.

Returning Authors Index

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.

Returning Institution Index

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.

The experience to innovation index

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:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Real-World Applications of Journal Topics

Understanding the theoretical concepts and research findings presented in the Journal of Computational Neuroscience is crucial. However, exploring the practical applications of these findings is equally as significant. Real-world application extends the boundaries of theoretical research, bridging the gap between theory and practice. It illuminates how concepts of Neuroscience, Artificial intelligence, Theory of computation, and other topics studied within the journal are applied in varied fields such as healthcare, technology, and education.

For instance, insights drawn from the field of Neuroscience have been instrumental in the progress of speech and language therapy. Specifically, in the role of a Speech-Language Pathologist, knowledge from Neuroscience is applied in diagnosing and treating various language and speech disorders. It is through understanding the complex interactions between different parts of the brain that these professionals can develop comprehensive treatment plans addressing each unique condition. Learn more about how to apply these concepts in a practical setting from this guide on how to be a speech therapist in Oklahoma.

Another intriguing example is the application of Artificial Intelligence (AI) in healthcare. AI, coupled with topics such as Visual Cortex and Receptive Field, can be useful in developing models to improve diagnostic accuracy in diseases affecting sight, predict patient outcomes, augment clinical decision-making, and personalize patient care. Remarkable advances in algorithms, machine learning, and computational theory have resulted in innovative healthcare applications such as image-based diagnostics, predictive modeling, and personalized medicine.

In summary, the real-world implications of the research topics in the Journal of Computational Neuroscience are vast and continually evolving, making the journal a vital source of knowledge not just for academic pursuits but also for practical applications that can influence and transform various industries.