Remarks on the Mathematical Modeling of Gene and Neuronal Networks by Ordinary Differential Equations

被引:1
|
作者
Ogorelova, Diana [1 ]
Sadyrbaev, Felix [1 ,2 ]
机构
[1] Daugavpils Univ, Fac Nat Sci & Math, Vienibas St 13, LV-5401 Daugavpils, Latvia
[2] Univ Latvia, Inst Math & Comp Sci, Rainis Blvd 29, LV-1459 Riga, Latvia
关键词
neuronal networks; dynamical systems; artificial networks; critical points; attractors; DYNAMICAL-SYSTEMS; NEURAL-NETWORKS;
D O I
10.3390/axioms13010061
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In the theory of gene networks, the mathematical apparatus that uses dynamical systems is fruitfully used. The same is true for the theory of neural networks. In both cases, the purpose of the simulation is to study the properties of phase space, as well as the types and the properties of attractors. The paper compares both models, notes their similarities and considers a number of illustrative examples. A local analysis is carried out in the vicinity of critical points and the necessary formulas are derived.
引用
收藏
页数:16
相关论文
共 50 条
  • [11] Collocation based training of neural ordinary differential equations
    Roesch, Elisabeth
    Rackauckas, Christopher
    Stumpf, Michael P. H.
    STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, 2021, 20 (02) : 37 - 49
  • [13] On limit measures and their supports for stochastic ordinary differential equations
    Xu, Tianyuan
    Chen, Lifeng
    Jiang, Jifa
    JOURNAL OF DIFFERENTIAL EQUATIONS, 2023, 365 : 72 - 99
  • [14] Modeling of neural systems and networks by functional differential equations
    Ermolaev, Valeriy
    Kropotov, Yuri
    Proskuryakov, Alexander
    2020 VI INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND NANOTECHNOLOGY (IEEE ITNT-2020), 2020,
  • [15] MODELING WITH STOCHASTIC DIFFERENTIAL EQUATIONS
    Garcia, Oscar
    MATHEMATICAL AND COMPUTATIONAL FORESTRY & NATURAL-RESOURCE SCIENCES, 2024, 16 (02): : 27 - 32
  • [16] Solving Ordinary Differential Equations With Adaptive Differential Evolution
    Zhang, Zijia
    Cai, Yaoming
    Zhang, Dongfang
    IEEE ACCESS, 2020, 8 : 128908 - 128922
  • [17] Nonlinear partial differential equations in neuroscience: From modeling to mathematical theory
    Carrillo, Jose A.
    Roux, Pierre
    MATHEMATICAL MODELS & METHODS IN APPLIED SCIENCES, 2025, 35 (02) : 403 - 584
  • [18] Numerical Methods That Preserve a Lyapunov Function for Ordinary Differential Equations
    Hernandez-Solano, Yadira
    Atencia, Miguel
    MATHEMATICS, 2023, 11 (01)
  • [19] Rigorous shadowing of numerical solutions of ordinary differential equations by containment
    Hayes, WB
    Jackson, KR
    SIAM JOURNAL ON NUMERICAL ANALYSIS, 2003, 41 (05) : 1948 - 1973
  • [20] Essential convergence rate of ordinary differential equations appearing in optimization
    Ushiyama, Kansei
    Sato, Shun
    Matsuo, Takayasu
    JSIAM LETTERS, 2022, 14 : 119 - 122