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Recent Advances in Neuroinformatics

This special issue belongs to the section “Computational Neuroscience, Neuroinformatics, and Neurocomputing“.

Special Issue Information

Dear Colleagues,

Understanding how the brain processes and transfers information across its various levels of organization is a crucial and fascinating question for comprehending both its healthy and pathological states. Thanks to advancements in computational neuroscience and neuroinformatics, which have bridged the gap between experiments and computers, we can now enhance our understanding of these phenomena.

This Special Issue aims to explore the latest developments in neuroinformatics, where the interface between experiments and computers can be leveraged to expand our knowledge of the brain. In particular, the continuous development in experimental procedures, where a huge amount of data can now be collected through numerous current channels and voltage imaging, create a need for a discussion of the most recent advances in neuroinformatics. This includes everything from collecting data and generating models using parameter estimation algorithms to analyzing brain data guided by mechanistic computer models. Submissions covering tools commonly applied in the intersection of data and models, such as Machine Learning, Deep Learning, Information Theory, and Dynamical Systems, are also welcome in this issue.

The topics that will be covered in this section include, but are not limited to:

  • Computational models based on in vitro and in vivo experimental data;
  • Stochastic neuron models that make intelligible the multiple noise sources observed in data;
  • Parameter estimation techniques and discussions of unidentifiability and degeneracy;
  • Theoretical methodologies that can be experimentally applied;
  • Computational tools to better explain experiments with models such as machine learning, deep learning, and information theory;
  • Models based on imaging techniques and big data recordings;
  • Integration and organization of massive and complex datasets;
  • Development of neuroengineering approaches and brain–computer interface.

We welcome contributions from experts in these areas and look forward to receiving your submissions.

Dr. Rodrigo F. O. Pena
Dr. Paulo R. Protachevicz
Prof. Dr. Ricardo F. Ferreira
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational neuroscience
  • experimental data analysis
  • neuroinformatics
  • brain–computer interface
  • neuroengineering
  • machine learning
  • stochastic neuron models
  • information theory
  • parameter estimation
  • imaging techniques

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Brain Sci. - ISSN 2076-3425