Neural Networks Journal

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About Neural Networks

Neural Networks is the archival journal of the world's three oldest neural modeling societies: the International Neural Network Society, the European Neural Network Society, and the Japanese Neural Network Society.

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 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, the Neural Networks Editorial Board represents experts in fields including psychology, neurobiology, computer science, engineering, mathematics, and physics.

The journal publishes articles, letters, reviews, and current opinions, as well as letters to the editor, book reviews, 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.

Subscriptions

The journal is published twelve times a year. Neural Networks can be accessed electronically via Science Direct, which is used by over eight million individuals worldwide.

All INNS members receive an online subscription to Neural Networks. To activate your online access, please click this link. You will need your INNS member number.

Neural Networks is now available via ContentsDirect, Elsevier Science's FREE online, e-mail alerting service. Approximately 2-4 weeks prior to each issue's publication you will receive the issue's table of contents - directly to your desktop. Register for this service here.

Submissions

Contributions to Neural Networks can now be submitted electronically by using the Elsevier EES system.

Instructions to authors can be found here.

Best Paper Award - Nominations Now Open

Neural Networks annually recognizes a single outstanding paper published in the journal. For the current round of competition, to be decided in 2022, any paper published in 2020 is eligible for consideration. The prize includes an award plaque and a $1,000 honorarium, to be split equally among the co-authors of the selected paper. No self-nomination is allowed, and no paper authored or co-authored by a Co-Editor-in-Chief is eligible for the Award.

For those who are interested in submitting a nomination for the Neural Networks Best Paper Award, the materials needed are the following:

Nomination Letter with the following information:

o Nominator: name, affiliation, and email address of nominator.

o Nominated Paper: full citation of the paper, authors and their affiliations, postal addresses and email addresses.

o Basis for Nomination: detailed documentation to justify the overall quality and impact of the paper (no more than 2 pages).

• Nominated paper in PDF format. The complete nomination packet must be saved in a single pdf file containing the above information in the given order. The name of the file must be surname_of_the_first_authorNN.pdf. The complete nomination packet must be submitted by email to a Co-Editor-in-Chief. Only when the Co-EIC acknowledges receipt of the nomination packet, the submission procedure can be considered complete.

The deadline is September 30, 2022.

The past Best Paper awardees are:

German Parisi, Ronald Kemker, Jose Part, Christopher Kanan, and Stefan Wermter: “Continual lifelong learning with neural networks: A review,” Neural Networks, volume 113, pp. 54-71, May 2019.
Xiao-Lei Zhang: "Multilayer bootstrap networks," Neural Networks, volume 103, pp. 29-43, July 2018.
Steven Grossberg: "Towards solving the hard problem of consciousness: The varieties of brain resonances and the conscious experiences that they support," Neural Networks, volume 87, pp. 38-95, March 2017.
Nikola Kasabov et al.: "Evolving Spatio-temporal Data Machines Based on the NeuCube Neuromorphic Framework: Design Methodology and Selected Applications," Neural Networks, volume 78, pp. 1-14, June 2016.
Jürgen Schmidhuber: "Deep Learning in Neural Networks: An Overview," Neural Networks, volume 61, pp. 85-117, January 2015.