Abstract
Multiple delays and connection topology are the key parameters for the realistic modeling of networks. This paper discusses the influences of time delays and connection weight on multi-delay artificial neural models with inertial couplings. Firstly, sufficient conditions of some singularities involving static bifurcation, Hopf bifurcation, and pitchfork-Hopf bifurcation are presented by analyzing the transcendental characteristic equation. Secondly, taking self-connection weight and coupling delays as adjusting parameters and utilizing the parameter perturbation with the aid of the non-reduced order technique for the first time, rich dynamics near zero-Hopf interaction are obtained on the plane with self-connected weight and coupling delay as abscissa and ordinate. The multi-delay inertial neural system can exhibit coexisting attractors such as a pair of nontrivial equilibrium points and a periodic orbit with nontrivial equilibrium points. Self-connected weight can affect the number and dynamics of the system equilibrium points, while time delays can contribute to both trivial equilibrium and non-trivial equilibrium losing their stability and generating limit cycles. Simulation plots are displayed with computer software to support the established main results. Compared with the traditional reduced-order method, the used method here is simple and valid with less computation. The key research findings of this paper have significant theoretical guiding value in dominating and optimizing networks.
Similar content being viewed by others
References
Angelaki DE, Correia MJ (1991) Models of membrane resonance in pigeon semicircular canal type II hair cells. Biol Cybernet 65:1–10
Ashmore JF, Attwell D (1985) Models for electrical tuning in hair cells. Proc Royal Soc London B 226:325–334
Babcock KL, Westervelt RM (1986) Stability and dynamics of simple electronic neural networks with added inertia. Physica D 23:464–469
Babcock KL, Westervelt RM (1987) Dynamics of simple electronic neural networks. Physica D 28:305–316
Bélair J, Campbell SA, Driessche P (1996) Frustration, stability, and delay-induced oscillations in a neural network model. SIAM J Appl Math 56(1):245–255
Bi Y, Li Y, Hou J, Liu Q (2021) Multiple time delays induced dynamics of p53 gene regulatory network. Int J Bifurcat Chaos 31(15):2150234
Ding S, Wang N, Bao H, Chen B, Wu H, Xu Q (2023) Memristor synapse-coupled piecewise-linear simplified Hopfield neural network: dynamics analysis and circuit implementation. Chaos Soliton Fract 166:112899
Dong T, Liao XF (2013) Hopf-Pitchfork bifurcation in a simplified BAM neural network model with multiple delays. J Comput Appl Math 253:222–234
Dong T, Xiang WL, Huang TW, Li HQ (2021) Pattern formation in a reaction-diffusion BAM neural network with time delay: (k1, k2) Mode Hopf-Zero Bifurcation Case. IEEE Trans Neural Netw Learn Systems. https://doi.org/10.1109/TNNLS.2021.3084693
Dong T, Gong XM, Huang TW (2022) Zero-Hopf Bifurcation of a memristive synaptic Hopfield neural network with time delay. Neural Netw 149:146–156
Érika D-P, Llibre J, Otero-Espinar MV, Valls C (2021) The zero-Hopf bifurcations in the Kolmogorov systems of degree 3 in R3. Commun Nonlinear Sci Numer Simulat 95:105621
Ge J (2022) Multi-delay-induced bifurcation singularity in two-neuron neural models with multiple time delays. Nonlinear Dyn 108:4357–4371
Ge J, Xu J (2012) Weak resonant double Hopf bifurcations in an inertial four-neuron model with time delay. Int J Neural Syst 22:63–75
Ge J, Xu J (2013) Stability switches and fold-Hopf bifurcations in an inertial four-neuron network model with coupling delay. Neurocomputing 110:70–79
Ge J, Xu J (2018) Stability and Hopf bifurcation on four-neuron neural networks with inertia and multiple delays. Neurocomputing 287:34–44
Guckenheimer J, Holmes P (1984) Nonlinear oscillations dynamical systems, and bifurcations of vector fields. J Appl Mech 51:947
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79:2554
Hopfield JJ (1984) Neurons with graded response have collective computational properties like two-state neurons. Proc Natl Acad Sci USA 81(10):3088–3092
Hou H, Zhang H (2023) Stability and hopf bifurcation of fractional complex–valued BAM neural networks with multiple time delays. Appl Math Comput 450:127986
Koch C (1984) Cable theory in neurons with active, linearized membranes. Biol Cybern 50(1):15–33
Liao H, Yang Z, Zhang Z, Zhou Y (2022) Finite-time synchronization for delayed inertial neural networks by the approach of the same structural functions. Neural Process Lett. https://doi.org/10.1007/s11063-022-11075-2
Liu Q, Liao X, Liu Y et al (2009a) Dynamics of an inertial two-neuron system with time delay. Nonlinear Dyn 58:573–609
Liu Q, Liao XF, Guo ST et al (2009b) Stability of bifurcating periodic solutions for a single delayed inertial neuron model under periodic excitation. Nonlinear Anal-Real 58:573–609
Ma J, Tang J (2017) A review for dynamics in neuron and neuronal network. Nonlinear Dyn 89:1569–1578
Marcus C, Westervelt R (1989) Stability of analog neural networks with delay. Phys Rev A 39:347–359
Mauro A, Conti F, Dodge F et al (1970) Subthreshold behavior and phenomenological impedance of the squid giant axon. J General Physiol 55:497–523
Parks PC (1962) A new proof of the Routh-Hurwitz stability criterion using the second method of Liapunov. Math Proc Cambridge 58(4):694–702
Shayer LP, Campbell SA (2000) Stability, bifurcation and multistability in a system of two coupled neurons with multiple time delays. SIAM J Appl Math 61(2):673–700
Song Z, Xu J (2022) Self-/mutual-symmetric rhythms and their coexistence in a delayed half-center oscillator of the CPG neural system. Nonlinear Dyn 108:2595–2609
Song Z, Wang C, Zhen B (2016) Codimension-two bifurcation and multistability coexistence in an inertial two-neuron system with multiple delays. Nonlinear Dyn 85:2099–2113
Song Z, Qian W, Zhen B, et al (2019) Multiple bifurcations and periodic coexistence in a delayed Hopfield two-neural system with a monotonic activation function. Adv. Differ. Equ. 167
Van der Pol B (1926) On relaxation-oscillations London Edinb. Dublin Phil Mag J Sci 2(11):978–992
Wang F, Liu M (2016) Global exponential stability of high-order bidirectional associative memory (BAM) neural networks with time delays in leakage terms. Neurocomputing 177:515–528
Wang T, Cheng Z, Bu R, Ma R (2019) Stability and Hopf bifurcation analysis of a simplified six-neuron tridiagonal two-layer neural network model with delays. Neurocomputing 332:203–214
Wheeler DW, Schieve WC (1997) Stability and chaos in an inertial two-neuron system. Physica D 105:267–284
Xing R, Xiao M, Zhang Y, Qiu J (2022) Stability and hopf bifurcation analysis of an (n + m)-neuron double-ring neural network model with multiple time delays. J Syst SCi Complex 35:159–178
Xu J, Chung KW, Chan CL (2007) An efficient method for studying weak resonant double Hopf bifurcation in nonlinear systems with delayed feedbacks. SIAM J Appl Dyn Syst 6(1):29–60
Xu Q, Ju Z, Ding S, Feng C, Chen M, Bao B (2022) Electromagnetic induction effects on electrical activity within a memristive Wilson neuron model. Cogn Neurodyn 16:1221–1231
Xu Q, Chen X, Chen B, Wu H, Li Z, Bao H (2023) Dynamical analysis of an improved FitzHugh-Nagumo neuron model with multiplier-free implementation. Nonlinear Dyn 111:8737–8749
Yang C, Liu Y, Li H (2022) Finite-time and fixed-time stabilization of multiple memristive neural networks with nonlinear coupling. Cogn Neurodyn 16:1471–1483. https://doi.org/10.1007/s11571-021-09778-8
Yao S, Ding L, Song Z, Xu J (2019) Two bifurcation routes to multiple chaotic coexistence in an inertial two-neural system with time delay. Nonlinear Dyn 95:1549–1563
Zhang Z, Cao J (2019) Novel finite-time synchronization criteria for inertial neural networks with time delays via integral inequality method. IEEE Trans Neural Netw Learn Syst 30(5):1476–1485
Acknowledgements
The author is very grateful to the editors and anonymous reviewers for their constructive comments and suggestions. This work was supported by Natural Science Foundation of Henan Province for Excellent Youth (Grant No. 212300410021), National Natural Science Foundation of China (Grant Nos. 11872175 and 62073122), and Young talents Fund of HUEL.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix A
Appendix A
Proof
For simplification, let
The solution (18) is rewritten as
One can obtain the following equations from (A.1)
Substituting Eqs. (A.1) to (A.5) into Eq. (17), the following three equations are derived as
That is,
where the expressions of \(F_{i} \left( {i = 1,2,3} \right)\) are consistent with those in Eq. (10).
This completes this proof of the lemma.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ge, J. Influences of time delay and connection topology on a multi-delay inertial neural system. Cogn Neurodyn 18, 615–630 (2024). https://doi.org/10.1007/s11571-023-10012-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11571-023-10012-w