Computational Intelligence and Neuroscience

Nature-Inspired Computing Applied to Neuroscience


Publishing date
01 Jan 2021
Status
Closed
Submission deadline
11 Sep 2020

Lead Editor

1University of Tunis el Manar, Tunis, Tunisia

2University of Tunis, Tunis, Tunisia

3, Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

4University of Aveiro, Aveiro, Portugal

This issue is now closed for submissions.
More articles will be published in the near future.

Nature-Inspired Computing Applied to Neuroscience

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Nature-inspired computing refers to computational methods that are mainly dedicated to the development of new models for studying how naturally occurring phenomena behave to address complex problems. This innovative research area encompasses several research branches, such as evolutionary computation, neural networks, artificial immune systems, and swarm intelligence.

There is a growing interest in applying nature-inspired methods to the analysis of biological data. As a consequence, researchers need to tackle the numerous challenges linked with bioinformatics and neurosciences through analyzing large amounts of heterogeneous complex biological and biomedical data and exploring new applications.

This Special Issue is dedicated to showcasing original research and review articles on nature-inspired methods applied to neurosciences, including bioinformatics. The objective of this Special Issue is to bridge the gap between theoretical researchers and practitioners, enabling the development of nature-inspired methods which meet the demands of real-world applications in various biomedical and neuroscience fields.

Potential topics include but are not limited to the following:

  • Nature-inspired computing in evolutionary neuroscience
  • Nature-inspired computing in gene expression data and microarrays (including clustering and biclustering)
  • Nature-inspired computing in functional magnetic resonance imaging (fMRI) data
  • Nature-inspired computing in single nucleotide polymorphism (SNP)
  • Nature-inspired computing in comparison and alignment
  • Nature-inspired computing in sequence analysis
  • Nature-inspired computing in gene selection
  • Nature-inspired computing in codon pair context
Computational Intelligence and Neuroscience
 Journal metrics
Acceptance rate28%
Submission to final decision79 days
Acceptance to publication37 days
CiteScore4.700
Impact Factor2.284
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