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Brain Sciences
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Neural Mechanisms of Brain Function: New Techniques and Computational Applications
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Neural Mechanisms of Brain Function: New Techniques and Computational Applications

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

Deadline for manuscript submissions: closed (30 July 2021) | Viewed by 14021

Special Issue Editor

Dr. Carlos Guerrero-Mosquera

E-Mail Website
Guest Editor
GTM Group, Carrer de Sant Joan de la Salle 42, Ramon Llull University, 08022 Barcelona, Spain
Interests: human brain connectivity; functional connectivity; EEG; intracranial EEG; phase synchronization; effective connectivity; nonlinear synchronization; electrophysiological connectivity; electrophysiological connectome; information based techniques; coherence

Special Issue Information

Dear Colleagues,

In the last years, computational neuroscience and signal processing have been utilized in discovering statistical patterns in different fields of science, including neuroscience. Now, algorithms and computational tools aim to identify, analyse, modelled and assess these patterns to provide important findings in different neuroscience applications.  This special thematic issue of Brain Sciences aims to assemble new theoretical approaches and computational solutions in discovering statistical patterns to analysis, diagnosis, and modelling of the neural mechanisms of brain functions.

We invite papers for a special issue: “Neural mechanisms of brain functions: new techniques and computational applications” in Brain Sciences Journal. This special issue welcomes contributions that engage new algorithms to analyse, diagnose, and modelled the neural mechanisms of brain functions. We welcome papers that computationally, methodologically and theoretically approach the growing importance of these algorithms in neuroscience research field.

We welcome submissions of original research papers from systems/cognitive and computational neuroscience, to neuroimaging and neural signal processing. Original research and reviews, as well as theoretical work, methods, and modelling articles are welcomed. The research work includes experimental studies using state-of-the-art in EEG and neuroimaging as well as experimentally-based computational or theoretical work and biologically inspired neural networks.

Dr. Carlos Guerrero-Mosquera
Guest Editor

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

  • signal processing
  • computational neuroscience
  • neuroimaging
  • machine learning
  • deep learning
  • neural networks
  • EEG
  • fNIRS
  • MEG
  • fMRI
  • functional connectivity

Published Papers (3 papers)

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Research

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14 pages, 5801 KiB  
Article
A Visual Encoding Model Based on Contrastive Self-Supervised Learning for Human Brain Activity along the Ventral Visual Stream
by Jingwei Li, Chi Zhang, Linyuan Wang, Penghui Ding, Lulu Hu, Bin Yan and Li Tong
Brain Sci. 2021, 11(8), 1004; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci11081004 - 29 Jul 2021
Cited by 3 | Viewed by 2568
Abstract
Visual encoding models are important computational models for understanding how information is processed along the visual stream. Many improved visual encoding models have been developed from the perspective of the model architecture and the learning objective, but these are limited to the supervised [...] Read more.
Visual encoding models are important computational models for understanding how information is processed along the visual stream. Many improved visual encoding models have been developed from the perspective of the model architecture and the learning objective, but these are limited to the supervised learning method. From the view of unsupervised learning mechanisms, this paper utilized a pre-trained neural network to construct a visual encoding model based on contrastive self-supervised learning for the ventral visual stream measured by functional magnetic resonance imaging (fMRI). We first extracted features using the ResNet50 model pre-trained in contrastive self-supervised learning (ResNet50-CSL model), trained a linear regression model for each voxel, and finally calculated the prediction accuracy of different voxels. Compared with the ResNet50 model pre-trained in a supervised classification task, the ResNet50-CSL model achieved an equal or even relatively better encoding performance in multiple visual cortical areas. Moreover, the ResNet50-CSL model performs hierarchical representation of input visual stimuli, which is similar to the human visual cortex in its hierarchical information processing. Our experimental results suggest that the encoding model based on contrastive self-supervised learning is a strong computational model to compete with supervised models, and contrastive self-supervised learning proves an effective learning method to extract human brain-like representations. Full article
(This article belongs to the Special Issue Neural Mechanisms of Brain Function: New Techniques and Computational Applications)
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22 pages, 2024 KiB  
Article
Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features
by Monika Kaczorowska, Małgorzata Plechawska-Wójcik and Mikhail Tokovarov
Brain Sci. 2021, 11(2), 210; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci11020210 - 9 Feb 2021
Cited by 19 | Viewed by 3196
Abstract
The paper is focussed on the assessment of cognitive workload level using selected machine learning models. In the study, eye-tracking data were gathered from 29 healthy volunteers during examination with three versions of the computerised version of the digit symbol substitution test (DSST). [...] Read more.
The paper is focussed on the assessment of cognitive workload level using selected machine learning models. In the study, eye-tracking data were gathered from 29 healthy volunteers during examination with three versions of the computerised version of the digit symbol substitution test (DSST). Understanding cognitive workload is of great importance in analysing human mental fatigue and the performance of intellectual tasks. It is also essential in the context of explanation of the brain cognitive process. Eight three-class classification machine learning models were constructed and analysed. Furthermore, the technique of interpretable machine learning model was applied to obtain the measures of feature importance and its contribution to the brain cognitive functions. The measures allowed improving the quality of classification, simultaneously lowering the number of applied features to six or eight, depending on the model. Moreover, the applied method of explainable machine learning provided valuable insights into understanding the process accompanying various levels of cognitive workload. The main classification performance metrics, such as F1, recall, precision, accuracy, and the area under the Receiver operating characteristic curve (ROC AUC) were used in order to assess the quality of classification quantitatively. The best result obtained on the complete feature set was as high as 0.95 (F1); however, feature importance interpretation allowed increasing the result up to 0.97 with only seven of 20 features applied. Full article
(This article belongs to the Special Issue Neural Mechanisms of Brain Function: New Techniques and Computational Applications)
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Other

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15 pages, 7342 KiB  
Perspective
Quantum and Electromagnetic Fields in Our Universe and Brain: A New Perspective to Comprehend Brain Function
by Zamzuri Idris, Zaitun Zakaria, Ang Song Yee, Diana Noma Fitzrol, Abdul Rahman Izaini Ghani, Jafri Malin Abdullah, Wan Mohd Nazaruddin Wan Hassan, Mohd Hasyizan Hassan, Asrulnizam Abdul Manaf and Raymond Ooi Chong Heng
Brain Sci. 2021, 11(5), 558; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci11050558 - 28 Apr 2021
Cited by 4 | Viewed by 7514
Abstract
The concept of wholeness or oneness refers to not only humans, but also all of creation. Similarly, consciousness may not wholly exist inside the human brain. One consciousness could permeate the whole universe as limitless energy; thus, human consciousness can be regarded as [...] Read more.
The concept of wholeness or oneness refers to not only humans, but also all of creation. Similarly, consciousness may not wholly exist inside the human brain. One consciousness could permeate the whole universe as limitless energy; thus, human consciousness can be regarded as limited or partial in character. According to the limited consciousness concept, humans perceive projected waves or wave-vortices as a waveless item. Therefore, human limited consciousness collapses the wave function or energy of particles; accordingly, we are only able to perceive them as particles. With this “limited concept”, the wave-vortex or wave movement comes into review, which also seems to have a limited concept, i.e., the limited projected wave concept. Notably, this wave-vortex seems to embrace photonic light, as well as electricity and anything in between them, which gives a sense of dimension to our brain. These elements of limited projected wave-vortex and limitless energy (consciousness) may coexist inside our brain as electric (directional pilot wave) and quantum (diffused oneness of waves) brainwaves, respectively, with both of them giving rise to one brain field. Abnormality in either the electrical or the quantum field or their fusion may lead to abnormal brain function. Full article
(This article belongs to the Special Issue Neural Mechanisms of Brain Function: New Techniques and Computational Applications)
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