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-computation
    Neural signal processing
    Brain-computer interfacing
    Neuron-electronics
    Neurofeedback, neural rehabilitation
    Neuroinformatics
    Brain waves, neuroimaging (fMRI, EEG, MEG, PET, NIR)
    Neural circuits: artificial and biological
    Neural control and neural system analysis
    Learning theory (supervised/unsupervised/reinforcement learning)
    Knowledge based neural networks, probabilistic, spatial, and temporal knowledge representation and reasoning
    Learning Classifiers
    Fusion of neural network- fuzzy systems- evolutionary algorithms
    Biologically inspired Intelligent agents (architectures, environments, adaptation/ learning and knowledge management)
    Bayesian networks and probabilistic reasoning
    Swarm intelligence, Ant colony optimization, Multi-agent systems
    Computational aspects of perceptual systems; Perception of different (visual, auditory and tactile) modalities; Perception and selective attention
    Long-term, Short-term, and Working memory
    Multi-level (neural, psychological, computational) analysis of cognitive phenomena
    Integrated theories of natural and artificial cognitive systems
    Information-theoretic, control-theoretic, and decision-theoretic approaches to neuroscience
    Multi-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 perspectives
    Neurobiologically inspired evolutionary systems

Featured 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.