Neural Networks

Aims & Scope

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

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	6.3
2024	6.00

SJR (SCImago Journal Rank)

Year	Value
2024	1.491

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	186

Journal Rank

Year	Value
2024	2413

Journal Citation Indicator

Year	Value
2024	10227

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science
• Medline
• Embase

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Neuroscience, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Multilayer feedforward networks are universal approximators

  Citation: 16738

  Authors: Kurt, Maxwell, Halbert

• Deep learning in neural networks: An overview

  Citation: 13642

  Authors: Jürgen

• Independent component analysis: algorithms and applications

  Citation: 6540

  Authors: A., E.

• Stacked generalization

  Citation: 5881

  Authors: David H.

• Approximation capabilities of multilayer feedforward networks

  Citation: 4296

  Authors: Kurt

• Framewise phoneme classification with bidirectional LSTM and other neural network architectures

  Citation: 4132

  Authors: Alex, Jürgen

• On the approximate realization of continuous mappings by neural networks

  Citation: 3197

  Authors: Ken-Ichi

• A scaled conjugate gradient algorithm for fast supervised learning

  Citation: 3106

  Authors: Martin Fodslette

• Probabilistic neural networks

  Citation: 2933

  Authors: Donald F.

• Networks of spiking neurons: The third generation of neural network models

  Citation: 2509

  Authors: Wolfgang

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