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
  • [21] Parameter uncertainty in biochemical models described by ordinary differential equations
    Vanlier, J.
    Tiemann, C. A.
    Hilbers, P. A. J.
    van Riel, N. A. W.
    MATHEMATICAL BIOSCIENCES, 2013, 246 (02) : 305 - 314
  • [22] On the applications of neural ordinary differential equations in medical image analysis
    Niu, Hao
    Zhou, Yuxiang
    Yan, Xiaohao
    Wu, Jun
    Shen, Yuncheng
    Yi, Zhang
    Hu, Junjie
    ARTIFICIAL INTELLIGENCE REVIEW, 2024, 57 (09)
  • [23] Hypocoercivity and controllability in linear semi-dissipative Hamiltonian ordinary differential equations and differential-algebraic equations
    Achleitner, Franz
    Arnold, Anton
    Mehrmann, Volker
    ZAMM-ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND MECHANIK, 2023, 103 (07):
  • [24] Reservoir Computing for Solving Ordinary Differential Equations
    Mattheakis, Marios
    Joy, Hayden
    Protopapas, Pavlos
    INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, 2023, 32 (01)
  • [25] Ordinary differential equations solution in kernel space
    Yazdi, Hadi Sadoghi
    Modaghegh, Hamed
    Pakdaman, Morteza
    NEURAL COMPUTING & APPLICATIONS, 2012, 21 : S79 - S85
  • [26] VarNet: Variational Neural Networks for the Solution of Partial Differential Equations
    Khodayi-mehr, Reza
    Zavlanos, Michael M.
    LEARNING FOR DYNAMICS AND CONTROL, VOL 120, 2020, 120 : 298 - 307
  • [27] Reconstructing gene regulatory networks of biological function using differential equations of multilayer perceptrons
    Mao, Guo
    Zeng, Ruigeng
    Peng, Jintao
    Zuo, Ke
    Pang, Zhengbin
    Liu, Jie
    BMC BIOINFORMATICS, 2022, 23 (01)
  • [28] Reconstructing gene regulatory networks of biological function using differential equations of multilayer perceptrons
    Guo Mao
    Ruigeng Zeng
    Jintao Peng
    Ke Zuo
    Zhengbin Pang
    Jie Liu
    BMC Bioinformatics, 23
  • [29] Benchmarks for identification of ordinary differential equations from time series data
    Gennemark, Peter
    Wedelin, Dag
    BIOINFORMATICS, 2009, 25 (06) : 780 - 786
  • [30] On the solution calculation of nonlinear ordinary differential equations via exact quadratization
    Carravetta, Francesco
    JOURNAL OF DIFFERENTIAL EQUATIONS, 2020, 269 (12) : 11328 - 11365