International Journal of Neural Systems

Aims & Scope

The International Journal of Neural Systems is a monthly rigorously peer-reviewed transdisciplinary journal which covers information processing in natural and artificial neural systems.

Subjects of special interests are machine learning, computational neuroscience and neurology, and innovative and high-impact articles that cross multiple fields including neurosciences and computer science and engineering.

Authors are strongly encouraged to review recent issues of the journal before submission.

The journal presents a fresh, undogmatic attitude towards this multi-disciplinary field, aiming to be a forum for novel ideas and improved understanding of collective and cooperative phenomena in systems with computational capabilities.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	6.4
2024	6.60

SJR (SCImago Journal Rank)

Year	Value
2024	1.579

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	79

Journal Rank

Year	Value
2024	2183

Journal Citation Indicator

Year	Value
2024	1527

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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

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Medicine, 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.

• GAUSSIAN PROCESSES FOR MACHINE LEARNING

  Citation: 1069

  Authors: MATTHIAS

• SPIKING NEURAL NETWORKS

  Citation: 812

  Authors: SAMANWOY, HOJJAT

• NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACES

  Citation: 661

  Authors: Erkki

• A Neural Network Method for Identification of Prokaryotic and Eukaryotic Signal Peptides and Prediction of their Cleavage Sites

  Citation: 545

  Authors: Henrik, Jacob, Søren, Gunnar Von

• PREDICTING THE FUTURE: A CONNECTIONIST APPROACH

  Citation: 465

  Authors: Andreas S., Bernardo A., David E.

• An Experimental Review on Deep Learning Architectures for Time Series Forecasting

  Citation: 392

  Authors: Pedro, Manuel, José C.

• A NEW METHOD FOR MAPPING OPTIMIZATION PROBLEMS ONTO NEURAL NETWORKS

  Citation: 342

  Authors: Carsten, Bo

• FREQUENCY RECOGNITION IN SSVEP-BASED BCI USING MULTISET CANONICAL CORRELATION ANALYSIS

  Citation: 340

  Authors: YU, GUOXU, JING, XINGYU, ANDRZEJ

• A HIGH-SPEED BRAIN SPELLER USING STEADY-STATE VISUAL EVOKED POTENTIALS

  Citation: 328

  Authors: MASAKI, YIJUN, YU-TE, YASUE, TZYY-PING

• KERNEL AND NONLINEAR CANONICAL CORRELATION ANALYSIS

  Citation: 323

  Authors: P. L., C.

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