Network-Computation in Neural Systems - Impact Factor

Network-Computation in Neural Systems - Taylor and Francis | 2024 Impact Factor:1.1 | Cite Score:3.7 | Q4

Network-Computation in Neural Systems Journal

Impact Factor and Journal Rank of Network-Computation in Neural Systems

About: Network: Computation in Neural Systems is a peer-reviewed journal dedicated to the study of computational neuroscience. The journal focuses on the development, analysis, and application of computational models to understand the functioning of neural systems. It serves as a forum for researchers to present their findings on the computational aspects of neural processing, providing insights into how neural systems compute and process information.
Impact and Significance
Network: Computation in Neural Systems is significant for several reasons:
Advancing Knowledge: It contributes to the advancement of computational neuroscience by publishing high-quality research that enhances our understanding of neural computation.
Interdisciplinary Research: The journal fosters interdisciplinary collaboration between neuroscientists, computer scientists, physicists, and mathematicians.
Technological Innovations: It highlights innovative computational methods and tools that address complex questions in neuroscience.
Practical Applications: The journal facilitates the application of computational models to practical problems in medicine, robotics, and artificial intelligence.
Global Reach: It provides a platform for researchers worldwide to share their findings and insights, promoting international collaboration.
Types of Articles Accepted
Network: Computation in Neural Systems accepts a variety of submission types, including:

Research Papers: Original research articles presenting new computational models, methods, and findings in neural computation.
Review Articles: Comprehensive reviews of the state-of-the-art in specific areas of computational neuroscience.
Technical Notes: Brief reports on novel computational techniques and tools.
Case Studies: Studies demonstrating the application of computational models to specific neural systems or behaviors.
Methodological Papers: Articles focusing on the development and validation of new computational methods.

Journal Home: Journal Homepage

Editor-in-Chief: Simon Stringe

Scope: Network: Computation in Neural Systems is a peer-reviewed journal that publishes high-quality research articles, reviews, and theoretical papers on the computational aspects of neural systems.
The journal covers a wide range of topics within the field of computational neuroscience, including but not limited to:
Neural Modeling: Research on mathematical and computational models of neural systems, including single neurons, neural circuits, and large-scale brain networks.
Neural Dynamics: Studies on the dynamic behavior of neural systems, including oscillations, synchronization, and neural coding.
Synaptic Plasticity: Research on the mechanisms and models of synaptic plasticity and its role in learning and memory.
Neural Computation: Studies on the computational principles underlying neural information processing and neural algorithms.
Neuroinformatics: Research on the development and application of computational tools and databases for managing and analyzing neural data.
Network Theory: Studies on the application of network theory to understanding the structure and function of neural systems.
Machine Learning and Artificial Intelligence: Research on the application of machine learning and AI techniques to neural data analysis and neural modeling.
Brain-Computer Interfaces: Studies on the development and application of brain-computer interfaces and neuroprosthetics.
Neural Development and Evolution: Research on the computational aspects of neural development, evolution, and comparative neuroanatomy.
Neural Disorders and Neuropharmacology: Studies on the computational modeling of neural disorders, drug effects, and therapeutic interventions.
Experimental Validation: Research that combines computational modeling with experimental data to validate and refine neural models.