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International Journal of Neural Systems is a peer-reviewed journal published by World Scientific, covering Neural Networks and Applications, EEG and Brain-Computer Interfaces, Neural dynamics and brain function.
| Publisher | World Scientific |
|---|---|
| Reference style | Superscript numbered (World Scientific) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. International Journal of Neural Systems 12, 45–58 (2023).
Formats any DOI in the closest standard style — International Journal of Neural Systems has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Neural Networks and Applications EEG and Brain-Computer Interfaces Neural dynamics and brain function Advanced Memory and Neural Computing Blind Source Separation Techniques |
| ISSN | 0129-0657 |
| Citation impact (2-yr) | 5.01 |
| h-index | 95 |
| i10-index | 1,033 |
| Total citations | 52,944 |
| Top institutions publishing here | Universidad de Granada |
| Journal website | www.worldscinet.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a…
Most current Artificial Neural Network (ANN) models are based on highly simplified brain dynamics. They have been used as powerful computational tools to solve complex pattern recognition, function estimation, and classification problems. ANNs have been evolving towards more powerful and more biologically realistic models. In the past decade, Spiking Neural Networks (SNNs) have been developed…
A single neuron with Hebbian-type learning for the connection weights, and with nonlinear internal feedback, has been shown to extract the statistical principal components of its stationary input pattern sequence. A generalization of this model to a layer of neuron units is given, called the Subspace Network, which yields a multi-dimensional, principal component subspace. This…
Separation of complex valued signals is a frequently arising problem in signal processing. For example, separation of convolutively mixed source signals involves computations on complex valued signals. In this article, it is assumed that the original, complex valued source signals are mutually statistically independent, and the problem is solved by the independent component analysis (ICA)…
We investigate the effectiveness of connectionist architectures for predicting the future behavior of nonlinear dynamical systems. We focus on real-world time series of limited record length. Two examples are analyzed: the benchmark sunspot series and chaotic data from a computational ecosystem. The problem of overfitting, particularly serious for short records of noisy data, is addressed…
