Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
2.9
Journals
Applied Sciences
Special Issues
Neural Networks and Brain Science: Structural Modeling, Functional Dynamics and...
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Neural Networks and Brain Science: Structural Modeling, Functional Dynamics and Applied Perspectives

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Neuroscience and Neural Engineering".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 1058

Editor

Prof. Dr. Vladimir Potop

E-Mail Website
Guest Editor
1. Department of Physical Education and Sport, Faculti of Sciences, Physical Education and Informatics, Doctoral School of Sport Science and Physical Education, National University of Science and Technology POLITEHNICA Bucharest—Pitești University Centre, 110040 Pitești, Romania
2. Institute of Physical Education and Sport, Moldova State University, Chisinau, Republic of Moldova
Interests: neural networks; motor control; biomechanics; sports performance analysis; neurocognitive processes; machine learning in sport science; neuromuscular adaptation; brain–behavior interactions

Special Issue Information

Dear Colleagues,

The Special Issue “Neural Networks and Brain Science: Structural Modeling, Functional Dynamics and Applied Perspectives” aims to provide an interdisciplinary platform for advanced research at the intersection of artificial neural networks, computational neuroscience, and the structural and functional organization of the human brain. Recent progress in deep learning, neuroimaging, neuromodulation, signal processing, and brain–computer interfaces has created unprecedented opportunities to understand neural mechanisms and develop intelligent systems inspired by biological principles. This Special Issue encourages contributions that explore structural modeling, functional mapping, neural computation, synaptic plasticity, cognitive processes, and the integration of biological and artificial systems.

Studies focusing on innovative methodologies, experimental approaches, simulations, and applied perspectives in domains such as motor control, sports science, rehabilitation, decision-making, perception, and behavior are especially welcome. By bringing together researchers from neuroscience, biomedical engineering, computer science, psychology, and sport science, this Special Issue seeks to promote the exchange of knowledge and highlight how neural models and brain-inspired algorithms can enhance both theoretical understanding and real-world applications. We invite original articles, reviews, and case studies that address emerging challenges and propose new directions for future research.

Prof. Dr. Vladimir Potop
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • neural networks
  • brain structure
  • brain function
  • computational neuroscience
  • neuroplasticity
  • neuroimaging
  • deep learning
  • cognitive processes
  • brain–computer interfaces
  • motor control

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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

Other

49 pages, 3542 KB  
Perspective
The DIME Architecture: A Unified Operational Algorithm for Neural Representation, Dynamics, Control and Integration
by Ionel Cristian Vladu, Nicu George Bîzdoacă, Ionica Pirici, Tudor-Adrian Bălșeanu and Eduard Nicușor Bondoc
Appl. Sci. 2026, 16(11), 5380; https://doi.org/10.3390/app16115380 - 27 May 2026
Viewed by 664
Abstract
Contemporary neuroscience has generated extensive empirical insights into perception, memory, prediction, valuation, and consciousness. However, it still lacks an explicit operational architecture capable of explaining how these processes emerge from a unified computational mechanism. This work introduces DIME (Detect–Integrate–Mark–Execute), a unified operational architecture [...] Read more.
Contemporary neuroscience has generated extensive empirical insights into perception, memory, prediction, valuation, and consciousness. However, it still lacks an explicit operational architecture capable of explaining how these processes emerge from a unified computational mechanism. This work introduces DIME (Detect–Integrate–Mark–Execute), a unified operational architecture in which perception, memory, valuation, and conscious access are treated as components of a single recurrent computational cycle. The framework is organized around four core elements: engrams, defined as distributed recurrent neural structures that support multiple activation trajectories rather than static memory traces; execution threads, representing temporally extended, causally coherent trajectories of neural activity; marker systems, corresponding to neuromodulatory and limbic mechanisms that regulate value, selection, plasticity, and trajectory competition; and hyperengrams, large-scale integrative states associated with global coordination and conscious access. Within this formulation, DIME provides a mapping between local neural assemblies, temporal sequence dynamics, value-based modulation, and large-scale network integration. Rather than treating perception, memory, and decision-making as partially independent processes, the framework interprets them as different expressions of a single operational loop acting across multiple spatial and temporal scales. The proposed architecture is consistent with empirical findings on hippocampal indexing, recurrent cortical processing, neuromodulatory control, and large-scale network dynamics, while remaining sufficiently general to support applications in artificial intelligence and robotics. Unlike frameworks centered on prediction, memory storage, or global broadcasting, DIME proposes that cognition arises from the recurrent interaction between executable representational structures, trajectory-based processing, value-guided selection, and dynamic large-scale integration. The framework generates explicit and falsifiable predictions regarding context-dependent neural trajectories, marker-mediated state transitions, and large-scale network reconfiguration. In this sense, DIME is not intended as a metaphorical synthesis, but as a testable architectural hypothesis for neuroscience and biologically inspired cognitive systems. Beyond theoretical neuroscience, the framework is also positioned as a transferable design-level reference model for adaptive AI systems, autonomous robotics, and cognitively informed engineering architectures operating in dynamic environments. Full article
(This article belongs to the Special Issue Neural Networks and Brain Science: Structural Modeling, Functional Dynamics and Applied Perspectives)
►▼ Show Figures

Figure 1

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