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Dynamic prediction modelling and equilibrium stability of a fractional discrete biophysical neuron model

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dc.contributor.author Al-Qurashi, Maysaa
dc.contributor.author Rashid, Saima
dc.contributor.author Jarad, Fahd
dc.contributor.author Ali, Elsiddeg
dc.contributor.author Egami, Ria H.
dc.date.accessioned 2023-12-05T13:49:02Z
dc.date.available 2023-12-05T13:49:02Z
dc.date.issued 2023-05
dc.identifier.citation Al-Qurashi, Maysaa...et.al. (2023). "Dynamic prediction modelling and equilibrium stability of a fractional discrete biophysical neuron model", Results in Physics, Vol.48. tr_TR
dc.identifier.issn 22113797
dc.identifier.uri http://hdl.handle.net/20.500.12416/6747
dc.description.abstract Here, we contemplate discrete-time fractional-order neural connectivity using the discrete nabla operator. Taking into account significant advances in the analysis of discrete fractional calculus, as well as the assertion that the complexities of discrete-time neural networks in fractional-order contexts have not yet been adequately reported. Considering a dynamic fast–slow FitzHugh–Rinzel (FHR) framework for elliptic eruptions with a fixed number of features and a consistent power flow to identify such behavioural traits. In an attempt to determine the effect of a biological neuron, the extension of this integer-order framework offers a variety of neurogenesis reactions (frequent spiking, swift diluting, erupting, blended vibrations, etc.). It is still unclear exactly how much the fractional-order complexities may alter the fring attributes of excitatory structures. We investigate how the implosion of the integer-order reaction varies with perturbation, with predictability and bifurcation interpretation dependent on the fractional-order β∈(0,1]. The memory kernel of the fractional-order interactions is responsible for this. Despite the fact that an initial impulse delay is present, the fractional-order FHR framework has a lower fring incidence than the integer-order approximation. We also look at the responses of associated FHR receptors that synchronize at distinctive fractional orders and have weak interfacial expertise. This fractional-order structure can be formed to exhibit a variety of neurocomputational functionalities, thanks to its intriguing transient properties, which strengthen the responsive neurogenesis structures. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1016/j.rinp.2023.106405 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Bursting Bifurcation tr_TR
dc.subject Discrete Fractional Operator tr_TR
dc.subject Fractional Difference Equation tr_TR
dc.subject Steady-States tr_TR
dc.subject Synchronization tr_TR
dc.title Dynamic prediction modelling and equilibrium stability of a fractional discrete biophysical neuron model tr_TR
dc.type article tr_TR
dc.relation.journal Results in Physics tr_TR
dc.contributor.authorID 234808 tr_TR
dc.identifier.volume 48 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümü tr_TR


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