Journal Guide

Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence, where neuroscientists, cognitive scientists, engineers, psychologists, physicists, computer scientists, and artificial intelligence investigators among others can publish their work in one periodical that bridges the gap between neuroscience, artificial intelligence and engineering.The journal provides research and review papers at an interdisciplinary level, with the field of intelligent systems for computational neuroscience as its focus. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. All items relevant to building theoretical and practical systems are within its scope, including contributions in the area of applicable neural networks theory, supervised and unsupervised learning methods, algorithms, architectures, performance measures, applied statistics, software simulations, hardware implementations, benchmarks, system engineering and integration and innovative applications.The journal spans the disciplines of computer science, mathematics, physics, psychology, cognitive science, medicine and neurobiology amongst others. Work on computational intelligence and neuroscience refers to work on theoretical and computational aspects of the development and functioning of the nervous system, which can be at the level of networks of neurons or at the cellular or the sub-cellular level.Topics of the journal include but are not limited to computational, theoretical, experimental, clinical and applied aspects of the following:Neural modeling and neural-computationNeural signal processingBrain-computer interfacingNeuron-electronicsNeurofeedback, neural rehabilitationNeuroinformaticsBrain waves, neuroimaging (fMRI, EEG, MEG, PET, NIR)Neural circuits: artificial and biologicalNeural control and neural system analysisLearning theory (supervised/unsupervised/reinforcement learning)Knowledge based neural networks, probabilistic, spatial, and temporal knowledge representation and reasoningLearning ClassifiersFusion of neural network- fuzzy systems- evolutionary algorithmsBiologically inspired Intelligent agents (architectures, environments, adaptation/ learning and knowledge management)Bayesian networks and probabilistic reasoningSwarm intelligence, Ant colony optimization, Multi-agent systemsComputational aspects of perceptual systems; Perception of different (visual, auditory and tactile) modalities; Perception and selective attentionLong-term, Short-term, and Working memoryMulti-level (neural, psychological, computational) analysis of cognitive phenomenaIntegrated theories of natural and artificial cognitive systemsInformation-theoretic, control-theoretic, and decision-theoretic approaches to neuroscienceMulti-disciplinary computational approaches to the study of creativity, learning, knowledge and inference, emotion and motivation, awareness and consciousness, perception and action, decision making and action, etc.Cognitive systems from artificial life, dynamical systems, complex systems perspectivesNeurobiologically inspired evolutionary systemsFeatured contributions will fall into original research papers or review articles. Articles are expected to be high quality contributions representing new and significant research, developments or applications of practical use and value. Decisions will be made based on originality, technical soundness, clarity of exposition, scientific contribution and multidisciplinary impact of the article.

Computational Linguistics is the premiere publication devoted exclusively to the design and analysis of natural language processing systems. From this unique open access quarterly, university and industry linguists, computational linguists, artificial intelligence (AI) investigators, cognitive scientists, speech specialists, and philosophers get information about computational aspects of research on language, linguistics, and the psychology of language processing and performance.

Computational Management Science is an international journal focusing on all computational aspects of management science. These include theoretical and empirical analysis of computational models: computational statistics: analysis and applications of constrained, unconstrained, robust, stochastic and combinatorial optimisation algorithms: dynamic models, such as dynamic programming and decision trees: new search tools and algorithms for global optimisation, modelling, learning and forecasting: models and tools of knowledge acquisition. The emphasis on computational paradigms is an intended feature of CMS, distinguishing it from more classical operations research journals.Officially cited as: Comput Manag Sci

The goal of Computational Materials Science is to report on results that provide new or unique insights into, or significantly expand our understanding of, the properties of materials or phenomena associated with their design, synthesis, processing, characterization, and utilization.Papers that report on modern materials modeling are of interest, including quantum chemical methods, density functional theory, semi-empirical and classical approaches, statistical mechanics, atomic-scale simulations, mesoscale modeling, phase-field techniques, and finite element methods. Not all topics that potentially fall under the category of computational materials science are appropriate for the journal. For example, submissions that focus on the design of components for structural applications, describe electrical behavior in a device, or characterize thermal or mass transport without extensive accompanying input and associated discussion from computational materials science methods of interest are best suited for other specialized journals.Reports of advances in technical methodologies, and the application of computational materials science to guide, interpret, inspire, or otherwise enhance related experimental materials research are of significant interest as long as the computational methods or results are a primary focus of the manuscript. Contributions on all types of materials systems will be considered in the form of articles, perspectives, and reviews.Benefits to authorsWe also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our support pages: http://support.elsevier.com

Computational Mathematics and Mathematical Physics is a monthly journal of the Russian Academy of Sciences (RAS). It contains the English translations of papers published in the Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki, which was founded in 1961 by Academician A. A. Dorodnitsyn. The Journal includes surveys and original papers on computational mathematics, computational methods of mathematical physics, informatics, and other mathematical sciences.

Computational Mathematics and Modeling focuses on important Russian contributions to computational mathematics that are useful to the applied scientist or engineer. This quarterly publication presents timely research articles by scientists from Moscow State University, an institution recognized worldwide for influential contributions to this subject. Numerical analysis, control theory, and the interplay of modeling and computational mathematics are among the featured topics.

Computational Mechanics reports original research in computational mechanics of enduring scholarly value. It focuses on areas that involve and enrich the rational application of mechanics, mathematics, and numerical methods in the practice of modern engineering. The journal investigates theoretical and computational methods and their rational applications. Areas covered include solid and structural mechanics, multi-body system dynamics, constitutive modeling, inelastic and finite deformation response, and structural control. The journal also covers fluid mechanics and fluid-structure interactions, biomechanics, free-surface and two-fluid flows, aerodynamics, fracture mechanics and structural integrity, multi-scale mechanics, particle and meshfree methods, transport phenomena, and heat transfer. Lastly, the journal publishes modern variational methods in mechanics in general.

CMFT is an international mathematics journal which publishes carefully selected original research papers in complex analysis (in a broad sense), and on applications or computational methods related to complex analysis. Survey articles of high standard and current interest can be considered for publication as well. Contributed papers should be written in English (exceptions in rare cases are tolerated), and in a lucid, expository style. Papers should not exceed 30 printed pages.

This journal publishes research on the analysis and development of computational algorithms and modeling technology for optimization. It examines algorithms either for general classes of optimization problems or for more specific applied problems, stochastic algorithms as well as deterministic algorithms. Computational Optimization and Applications covers a wide range of topics in optimization, including: large scale optimization, unconstrained optimization, constrained optimization, nondifferentiable optimization, combinatorial optimization, stochastic optimization, multiobjective optimization, and network optimization. It also covers linear programming, complexity theory, automatic differentiation, approximations and error analysis, parametric programming and sensitivity analysis, management science, and more. This peer-reviewed journal features both research and tutorial papers that provide theoretical analysis, along with carefully designed computational experiments.Officially cited as: Comput Optim Appl

Computational Psychiatry publishes original research articles and reviews that involve the application, analysis, or invention of theoretical, computational and statistical approaches to mental function and dysfunction. Topics include brain and behavioral modeling over multiple scales and levels of analysis, and the use of these models to understand psychiatric dysfunction, its remediation, its relation to social or biological factors, and the development and sustenance of healthy cognition throughout the lifespan.