Journal Information
Impact Factor:

Call For Papers
Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.

Neurocomputing welcomes theoretical contributions aimed at winning further understanding of neural networks and learning systems, including, but not restricted to, architectures, learning methods, analysis of network dynamics, theories of learning, self-organization, biological neural network modelling, sensorimotor transformations and interdisciplinary topics with artificial intelligence, artificial life, cognitive science, computational learning theory, fuzzy logic, genetic algorithms, information theory, machine learning, neurobiology and pattern recognition.

Neurocomputing covers practical aspects with contributions on advances in hardware and software development environments for neurocomputing, including, but not restricted to, simulation software environments, emulation hardware architectures, models of concurrent computation, neurocomputers, and neurochips (digital, analog, optical, and biodevices).

Neurocomputing reports on applications in different fields, including, but not restricted to, signal processing, speech processing, image processing, computer vision, control, robotics, optimization, scheduling, resource allocation and financial forecasting.

Neurocomputing publishes reviews of literature about neurocomputing and affine fields.

Neurocomputing reports on meetings, including, but not restricted to, conferences, workshops and seminars.

Neurocomputing reports on functionality/availability of software, on comparative assessments, and on discussions of neurocomputing software issues.

Now also including: Neurocomputing Letters - for the rapid publication of special short communications.
Last updated by Xin Yao in 2017-11-12
Special Issues
Special Issue on Deep Learning Neural Networks: Methods, Systems, and Applications
Submission Date: 2018-03-31

Neural networks (NNs) and deep learning (DL) currently provide the best solutions to many problems in image recognition, speech recognition, natural language processing, control and precision health. NN and DL make the artificial intelligence (AI) much closer to human thinking modes. However, there are many open problems related to DL in NN, e.g.: convergence, learning efficiency, optimality, multi-dimensional learning, on-line adaptation. This requires to create new algorithms and analysis methods. Practical applications both require and stimulate this development. The aim of this special issue of Neurocomputing is to showcase state-of-the-art work in the field of deep learning neural networks including their methods, systems, and applications. Original papers related are welcome. The list of possible topics includes, but is not limited to: l New deep learning algorithms l New neural network architectures for deep learning l Hierarchical deep learning l Multi-dimensional deep learning l Deep learning of spatio-temporal data l On-line deep learning neural networks l Neuromorphic deep learning architectures l Better combinations of existing algorithms and techniques for deep learning l Combining policy learning, value learning, and model-based search l Data-driven deep learning and control l Optimization by deep neural networks l Optimization and optimal decision in games by deep learning l Mathematical analysis of deep learning (regarding convergence, optimality, stability, robustness, adaptability and so on) l Applications of deep learning algorithms, architectures, and systems to robotics, control, data analysis, prediction and forecast, modeling and simulation, precision health, and other.
Last updated by Dou Sun in 2017-11-02
Special Issue on Deep Understanding of Big Multimedia Data
Submission Date: 2018-04-01

During the past decade, large-scale multimedia data (e.g., video, images, audios, text) can be easily collected in different fields and pattern discovery from these raw data has been attracting increasing interests in the multimedia domain. Semantically understanding multimedia data can substantially enhance their practical applications. In reality, current multimedia techniques still cannot provide satisfied understanding of multimedia data. There still exists a gap between extracting representations (or knowledge) from big multimedia data and practical demands. In this case, data-driven understanding through shallow models and deep learning methods is very important and has been attracting a huge number of interests in data science and artificial intelligence. Goals and topics This special issue provides a forum for researchers to focus on their study in the new trends of deep understanding of big multimedia data and their applications. To do this, we invite papers (including a survey paper) to address the challenges in understanding of big multimedia data. The list of possible topics includes, but not limited to: l. 3D multimedia data understanding: - Feature extraction of 3D multimedia data via sequential dictionary learning, convolutional neural networks (CNN) and recurrent neural networks (RNN) - Video/audio data analysis via supervised learning, unsupervised learning, semi-supervised learning methods, and Long Short Term Memory networks - Video/audio captioning, tracking via sparse learning or deep learning methods 2. 2D multimedia data understanding - Understanding and analysis of multimedia data (e.g., segmentations, prediction, and diagnosis/prediction) via shallow learning and deep learning - Tools and applications for medicine and healthcare data (e.g. clustering, storing, ranking, hashing, and retrieval) 3. Multi-modal multimedia data understanding - Deep pattern discovery from single-modal multimedia data - Knowledge integration of multi-modal data through transfer learning and deep neural network - Shallow/deep data fusion combing 2D multimedia data and 3D multimedia data It is noteworthy that above topics have been widely and recently published in related major conferences, which serve as strong evidence of the potential paper submission sources to our special issue, including: ACM Special Interest Group on Management of Data (SIGMOD), ACM SIGKDD, Conference on Knowledge Discovery and Data Mining (KDD), International Joint Conferences on Artificial Intelligence (IJCAI), Association for the Advancement of Artificial Intelligence (AAAI), Neural Information Processing Systems (NIPS), International Conference on Machine Learning (ICML), ACM Multimedia (MM), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision (ICCV), etc.
Last updated by Dou Sun in 2017-12-16
Special Issue on Virtual Images for Visual Artificial Intelligence
Submission Date: 2018-04-15

Recently, deep learning has become one of the core technologies of computer vision and artificial intelligence. Deep learning is a data-driven technology and its performance heavily relies on large-scale labeled data, e.g., ImageNet and MS COCO. Unfortunately, it is rather expensive to collect and annotate large-scale image data from the real world, the collected real images are limited in covering complex environmental conditions, and the real scenes are uncontrollable and unrepeatable. As a result, the performance of current deep learning systems is not satisfactory while working in complex scenarios, such as autonomous driving and intelligent monitoring scenarios. In light of the disadvantages of collecting images from the real world, more and more researchers start to synthesize and use virtual images for computer vision research. A variety of advanced techniques including computer graphics simulation, image style transfer, and generative models have been used for virtual image generation. The virtual images are especially valuable to the training, testing, understanding and optimization of learning-based models in computer vision. This special issue emphasizes the important role of virtual images in deep learning and computer vision research, and welcomes a broad range of submissions developing and using virtual images for visual artificial intelligence. The list of possible topics includes, but is not limited to: - New image synthesis methods - Computer graphics and virtual/augmented reality for scene simulation - Graphics rendering techniques for generating photorealistic virtual images - Image-to-image translation and video-to-video translation - Image super-resolution - Deep generative models related to virtual images (variational autoencoders, generative adversarial networks, etc.) - Neural networks that learn from virtual images - Domain adaptation methods for deep learning - Understanding deep architectures using virtual images - Intelligent visual computing with virtual images - Virtual-real interactive parallel vision and parallel imaging - Virtual images and artistic creation - Applications of virtual images to intelligent systems (robots, autonomous vehicles, visual monitoring systems, medical devices, and so on)
Last updated by Dou Sun in 2017-12-16
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