Methods, Analysis and Applications in Computational Neuroscience

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 1266

Special Issue Editor


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Guest Editor
Instituto Politécnico Nacional, ESIME Culhuacan, Av. Santa Ana No. 1000, Ciudad de Mexico 04260, Mexico
Interests: spiking neural P systems; spiking neural networks; neuromorphic architectures

Special Issue Information

Dear Colleagues,

Nowadays, bio-inspired algorithms play an important role in the development of modern applications. In particular, applications such as control theory, cybernetics, quantitative psychology, machine learning, artificial neural networks, artificial intelligence and computational learning theory have been significantly improved since most of them are highly inspired by diverse neural phenomena. Therefore, the study of the complex biophysical characteristics of neurons potentially allows for the development of better neurobiological computational systems. Inspired by differing dynamics and modulations, among others, recent neural algorithms have been created. These models have been proven to improve the computational capabilities of conventional systems. In addition, most of them have been used in practical applications.

This Special Issue aims to publish significant contributions considering theoretical approaches and practical problems in computational neuroscience. Topics of interest include, but are not limited to, the following:

  • Behaviors of networks;
  • Development, axonal patterning and guidance;
  • Sensory processing;
  • Motor control;
  • Memory and synaptic plasticity;
  • Visual attention, identification and categorization;
  • Cognition, discrimination and learning;
  • Computational clinical neuroscience;
  • Predictive computational neuroscience;
  • Neuromorphic computing;
  • Neuroinformatics;
  • Software for neuroscience;
  • Cybernetics.

I look forward to receiving your contributions.

Dr. Giovanny Sánchez
Guest Editor

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Keywords

  • large-scale neural networks
  • axon and dendrites
  • neuromorphic architectures
  • synaptic transmission

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Published Papers (1 paper)

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Research

10 pages, 2297 KiB  
Article
New High-Speed Arithmetic Circuits Based on Spiking Neural P Systems with Communication on Request Implemented in a Low-Area FPGA
by José Rangel, Esteban Anides, Eduardo Vázquez, Giovanny Sanchez, Juan-Gerardo Avalos, Gonzalo Duchen and Linda K. Toscano
Mathematics 2024, 12(22), 3472; https://doi.org/10.3390/math12223472 - 7 Nov 2024
Viewed by 910
Abstract
During the last years, the demand for internet-of-things (IoT) resource-constrained devices has grown exponentially. To address this need, several digital methods have been proposed to improve these devices in terms of area and power consumption. Despite achieving significant results, improvement in these factors [...] Read more.
During the last years, the demand for internet-of-things (IoT) resource-constrained devices has grown exponentially. To address this need, several digital methods have been proposed to improve these devices in terms of area and power consumption. Despite achieving significant results, improvement in these factors is still a challenging task. Recently, an emerging computational area has been seen as a potential solution to improving the performance of conventional binary circuits. In particular, this area uses a method based on spiking neural P systems (SN P) to create arithmetic circuits, such as adders, subtractors, multipliers, and divisors, since these components are vital in many IoT applications. To date, several efforts have been dedicated to decreasing the number of neurons and synapses to create compact circuits. However, processing speed is a persistent issue. In this work, we propose four compact arithmetic circuits with high processing speeds. To evaluate their performance, we designed a neuromorphic processor that is capable of performing four operations using dynamic connectivity. As a consequence, the proposed neuromorphic processor achieves higher processing speeds by maintaining low area consumption in comparison with the existing approaches. Full article
(This article belongs to the Special Issue Methods, Analysis and Applications in Computational Neuroscience)
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