ICONIP 2014 - 21st International Conference on Neural Information Processing

Conference Scope and Themes
Prospective authors are invited to contribute high-quality papers related to the topics listed below. The scope of the conference includes, but is not limited to, the following areas :
Cognitive Science and Artificial Intelligence:
Theoretical, computational, or experimental studies of perception, psychophysics, learning and memory, inference and reasoning, problem solving, natural language processing, and neuropsychology.
Learning Theory, Algorithms, and Architectures:
Stability and convergence analysis, statistical learning algorithms, neural network models, supervised learning, unsupervised learning, reinforcement learning, kernel methods, graphical models, dimensionality reduction and manifold learning, statistical and information-theoretic methods, generalization, regularization and model selection, Gaussian processes and mixture models, matrix/tensor analysis, statistical physics of learning, and evolutionary algorithms.
Computational Neuroscience and Brain Imaging:
Theoretical and experimental studies of processing and transmission of information in biological neurons and networks, spiking neurons, visual and auditory cortex, neural encoding and decoding, plasticity and adaptation, brain imaging, neuroimaging, brain mapping, and brain segmentation.
Vision, Speech and Signal Processing:
Biological and machine vision, image processing and coding, visual perception and modeling, visual selective attention, visual coding and representation, object detection and recognition, motion detection and tracking, natural scene analysis, image processing, auditory perception and modeling, source separation, speech recognition and speech synthesis, speaker identification, and audio & speech retrieval.
Control, Robotics and Hardware Technologies:
Decision and control, exploration, planning, navigation, Markov decision processes, game playing, multi-agent coordination, neuro-fuzzy system, cognitive robotics, developmental robotics, analog and digital VLSI, neuromorphic engineering, computational sensors and actuators, microrobotics, bioMEMS, neural prostheses, photonics, and molecular & quantum computing.
Novel approaches and Applications:
Innovative applications that use machine learning, including systems for time series prediction, bioinformatics, systems biology, text/web analysis, multimedia processing, adaptive intelligent systems, brain-computer interfaces, granular computing, hybrid intelligent systems, neuroinformatics and neuroengineering, bioinformatics, information retrieval, data mining, and knowledge discovery.