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

dc.contributor.authorAl-Qurashi, Maysaa
dc.contributor.authorRashid, Saima
dc.contributor.authorJarad, Fahd
dc.contributor.authorAli, Elsiddeg
dc.contributor.authorEgami, Ria H.
dc.contributor.authorID234808tr_TR
dc.date.accessioned2023-12-05T13:49:02Z
dc.date.available2023-12-05T13:49:02Z
dc.date.issued2023
dc.departmentÇankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractHere, 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.en_US
dc.description.publishedMonth5
dc.identifier.citationAl-Qurashi, Maysaa...et.al. (2023). "Dynamic prediction modelling and equilibrium stability of a fractional discrete biophysical neuron model", Results in Physics, Vol.48.en_US
dc.identifier.doi10.1016/j.rinp.2023.106405
dc.identifier.issn22113797
dc.identifier.urihttp://hdl.handle.net/20.500.12416/6747
dc.identifier.volume48en_US
dc.language.isoenen_US
dc.relation.ispartofResults in Physicsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBursting Bifurcationen_US
dc.subjectDiscrete Fractional Operatoren_US
dc.subjectFractional Difference Equationen_US
dc.subjectSteady-Statesen_US
dc.subjectSynchronizationen_US
dc.titleDynamic prediction modelling and equilibrium stability of a fractional discrete biophysical neuron modeltr_TR
dc.titleDynamic Prediction Modelling and Equilibrium Stability of a Fractional Discrete Biophysical Neuron Modelen_US
dc.typeArticleen_US
dspace.entity.typePublication

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