Other meanings of neural network
Academic journal
Neural Networks is a peer-reviewed scientific journal covering research on artificial neural networks and related fields. It is the official journal of the International Neural Network Society, the European Neural Network Society, and the Japanese Neural Network Society. Published monthly by Elsevier, it was established in 1988 and is indexed in major databases. The journal emphasizes both theoretical and applied aspects of neural computation, including learning algorithms, architectures, and applications in pattern recognition, control, and neuroscience.
Neural Networks covers a broad range of topics in neural computation, including artificial neural networks, computational neuroscience, and cognitive science. The journal was launched in 1988 under the founding editorship of Stephen Grossberg, a prominent figure in the field. It quickly became a leading venue for research on self-organizing maps, adaptive resonance theory, and other biologically inspired models. The journal also publishes special issues on emerging topics, such as deep learning and neuromorphic computing.
The journal operates a rigorous peer-review process, with an editorial board composed of international experts. It is the official journal of three major societies: the International Neural Network Society (INNS), the European Neural Network Society (ENNS), and the Japanese Neural Network Society (JNNS). This affiliation ensures broad international representation and high editorial standards. The journal also offers open access options, with a hybrid model that allows authors to publish under a Creative Commons license for a fee.
Neural Networks is indexed in major scientific databases, including Science Citation Index, Scopus, and PubMed. Its impact factor has fluctuated over the years, reflecting the dynamic nature of the field. The journal is widely cited in both artificial intelligence and neuroscience literature. It also provides a platform for interdisciplinary research, bridging the gap between machine learning and brain science.
Beyond its main scope, the journal has published seminal papers on topics like spiking neural networks and reservoir computing. It has also featured special issues on neural network applications in robotics and bioinformatics. The journal's archives contain early work on convolutional networks and backpropagation, which have become foundational in modern deep learning. Additionally, the journal has occasionally published historical retrospectives and reviews of classic papers, offering insight into the evolution of the field.
This article is about the academic journal. For the general concept, see Artificial neural network.
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