Mathematical Models and Novel Data-Analyzing Methods in Neuroscience

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 813

Special Issue Editors

Department of Neuroscience, The City University of Hong Kong, Hong Kong, China
Interests: neural field models; synaptic plasticity; mathematical models for neural systems; brain-inspired machine learning
1. Lyda Hill Department of Bioinformatics, O'Donnell Brain Institute, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, USA
2. O’Donnell Brain Institute, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, USA
Interests: nonlinear dynamics; bayesian inference; neural coding; information theory; representation theory

Special Issue Information

Dear Colleagues,

Neuroscience is one of the fastest growing elements of natural science. Recent advances in neurotechniques have greatly accelerated neuroscience research and accumulated a vast amount of data. However, even with the growing numbers of neuroscience experiments, these experimental studies are not yet sufficient to unveil the functional meaning of the observations made, or their underlying mechanisms. Data analyzing methods and mathematical models are essential approaches to understanding the dynamics of neural systems and their implications in information processing. Therefore, this Special Issue aims to present the recent studies on both mathematical models and data-analyzing methods in neuroscience. Those studies include, but are not limited to, novel methods analyzing electrophysiology data, models for subcellular structure, synapses, neurons, neuroglia cells, and neuronal networks. Additionally, abstract models and brain-inspired learning algorithms are welcome.

Dr. Chi Chung Alan Fung
Dr. Wen-Hao Zhang
Guest Editors

Manuscript Submission Information

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Keywords

  • computational neuroscience
  • neuronal models
  • synaptic models
  • neural networks
  • brain-inspired algorithms
  • abstract models in neuroscience
  • Bayesian inference
  • neural coding
  • neural data analysis

Published Papers (1 paper)

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Research

11 pages, 6846 KiB  
Article
A Network-Level Stochastic Model for Pacemaker GABAergic Neurons in Substantia Nigra Pars Reticulata
by Karine Guimarães and Aline Duarte
Mathematics 2023, 11(17), 3778; https://doi.org/10.3390/math11173778 - 03 Sep 2023
Viewed by 623
Abstract
In this paper we present computational simulations of a mathematical model describing the time evolution of membrane potentials in a GABAergic neural network. This model, with stochastic and evolutionary characteristics, is an application of the version introduced previously where the authors present the [...] Read more.
In this paper we present computational simulations of a mathematical model describing the time evolution of membrane potentials in a GABAergic neural network. This model, with stochastic and evolutionary characteristics, is an application of the version introduced previously where the authors present the continuous time version of a new class of stochastic models for biological neural networks. The goal is to computationally simulate the model (with the interaction conditions of a GABAergic network) and make biological inferences. More specifically, the computational simulations of the model that describe spiking neurons with electrophysiological characteristics of a brain region called substantia nigra pars reticulata, emphasize changes in desynchronized firing activity and how changes in individual activity propagate through the network. Full article
(This article belongs to the Special Issue Mathematical Models and Novel Data-Analyzing Methods in Neuroscience)
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