Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.9
3.3
Journals
Brain Sciences
Special Issues
Recent Advances in Neuroinformatics
share announcement

Recent Advances in Neuroinformatics

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Computational Neuroscience and Neuroinformatics".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 4075

Special Issue Editors

Dr. Rodrigo Pena

E-Mail Website
Guest Editor
Department of Biological Sciences, Florida Atlantic University, John D. MacArthur Campus, Jupiter, FL, USA
Interests: computational neuroscience; neural noise; neurodynamics; information theory; machine learning; deep learning; resonance
Dr. Paulo R. Protachevicz

E-Mail Website
Guest Editor
Institute for Complex Systems and Mathematical Biology, SUPA, University of Aberdeen, Aberdeen, UK
Interests: computational neuroscience; neuronal dynamics; long and short-term plasticity; synchronization; theoretical neuroscience; brain diseases; external driven neural networks
Prof. Dr. Ricardo F. Ferreira

E-Mail Website
Guest Editor
Statistics Department, Federal University of São Carlos, São Carlos, Brazil
Interests: information theory; statistical analysis of neural data; statistical model selection for stochastic systems; stochastic modeling in neuroscience

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

17 pages, 2415 KiB  
Article
The Roles of Potassium and Calcium Currents in the Bistable Firing Transition
by Fernando S. Borges, Paulo R. Protachevicz, Diogo L. M. Souza, Conrado F. Bittencourt, Enrique C. Gabrick, Lucas E. Bentivoglio, José D. Szezech, Jr., Antonio M. Batista, Iberê L. Caldas, Salvador Dura-Bernal and Rodrigo F. O. Pena
Brain Sci. 2023, 13(9), 1347; https://doi.org/10.3390/brainsci13091347 - 20 Sep 2023
Viewed by 1280
Abstract
Healthy brains display a wide range of firing patterns, from synchronized oscillations during slow-wave sleep to desynchronized firing during movement. These physiological activities coexist with periods of pathological hyperactivity in the epileptic brain, where neurons can fire in synchronized bursts. Most cortical neurons [...] Read more.
Healthy brains display a wide range of firing patterns, from synchronized oscillations during slow-wave sleep to desynchronized firing during movement. These physiological activities coexist with periods of pathological hyperactivity in the epileptic brain, where neurons can fire in synchronized bursts. Most cortical neurons are pyramidal regular spiking (RS) cells with frequency adaptation and do not exhibit bursts in current-clamp experiments (in vitro). In this work, we investigate the transition mechanism of spike-to-burst patterns due to slow potassium and calcium currents, considering a conductance-based model of a cortical RS cell. The joint influence of potassium and calcium ion channels on high synchronous patterns is investigated for different synaptic couplings (gsyn) and external current inputs (I). Our results suggest that slow potassium currents play an important role in the emergence of high-synchronous activities, as well as in the spike-to-burst firing pattern transitions. This transition is related to the bistable dynamics of the neuronal network, where physiological asynchronous states coexist with pathological burst synchronization. The hysteresis curve of the coefficient of variation of the inter-spike interval demonstrates that a burst can be initiated by firing states with neuronal synchronization. Furthermore, we notice that high-threshold (IL) and low-threshold (IT) ion channels play a role in increasing and decreasing the parameter conditions (gsyn and I) in which bistable dynamics occur, respectively. For high values of IL conductance, a synchronous burst appears when neurons are weakly coupled and receive more external input. On the other hand, when the conductance IT increases, higher coupling and lower I are necessary to produce burst synchronization. In light of our results, we suggest that channel subtype-specific pharmacological interactions can be useful to induce transitions from pathological high bursting states to healthy states. Full article
(This article belongs to the Special Issue Recent Advances in Neuroinformatics)
►▼ Show Figures

Figure 1

Review

Jump to: Research

20 pages, 1812 KiB  
Review
Data Mining of Microarray Datasets in Translational Neuroscience
by Lance M. O’Connor, Blake A. O’Connor, Jialiu Zeng and Chih Hung Lo
Brain Sci. 2023, 13(9), 1318; https://doi.org/10.3390/brainsci13091318 - 14 Sep 2023
Viewed by 1711
Abstract
Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the [...] Read more.
Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases. Full article
(This article belongs to the Special Issue Recent Advances in Neuroinformatics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop