Neural Networks

The Official Journal of the International Neural Network Society, European Neural Network Society & Japanese Neural Network Society

Neural Networks - ISSN 0893-6080
Source Normalized Impact per Paper (SNIP): 2.236 Source Normalized Impact per Paper (SNIP):
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR): 1.629 SCImago Journal Rank (SJR):
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.
Impact Factor: 5.287 (2016) Impact Factor:
The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
© Thomson Reuters Journal Citation Reports 2015
5 Year Impact Factor: 4.028 (2016) Five-Year Impact Factor:
To calculate the five year Impact Factor, citations are counted in 2014 to the previous five years and divided by the source items published in the previous five years.
© Journal Citation Reports 2015, Published by Thomson Reuters
Volumes: Volumes 85-96
Issues: 12 issues
ISSN: 08936080

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Description

Neural Networks is the archival journal of the world's three oldest neural modeling societies: the International Neural Network Society (http://www.inns.orgINNS), the European Neural Network Society (http://www.e-nns.org/ENNS), and the Japanese Neural Network Society (http://www.jnns.org/english/JNNS). A subscription to the journal is included with membership in each of these societies.

Neural Networks provides a forum for developing and nurturing an international community of scholars and practitioners who are interested in all aspects of neural networks and related approaches to computational intelligence. Neural Networks welcomes high quality http://ees.elsevier.com/neunet/submissions that contribute to the full range of neural networks research, from behavioral and brain modeling, learning algorithms, through mathematical and computational analyses, to engineering and technological applications of systems that significantly use neural network concepts and techniques. This uniquely broad range facilitates the cross-fertilization of ideas between biological and technological studies, and helps to foster the development of the interdisciplinary community that is interested in biologically-inspired computational intelligence. Accordingly, Neural Networks http://www.journals.elsevier.com/neural-networks/editorial-board/editorial board represents experts in fields including psychology, neurobiology, computer science, engineering, mathematics, and physics. The journal publishes articles, letters and reviews, as well as letters to the editor, editorials, current events, software surveys, and patent information. Articles are published in one of five sections: Cognitive Science, Neuroscience, Learning Systems, Mathematical and Computational Analysis, Engineering and Applications.

The journal is published twelve times a year. Neural Networks can be accessed electronically via Science Direct (http://www.sciencedirect.com/science/journal/08936080), which is used by over eight million individuals world-wide.

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