Dynamic prediction modelling and equilibrium stability of a fractional discrete biophysical neuron model
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.contributor.authorID | 234808 | tr_TR |
dc.date.accessioned | 2023-12-05T13:49:02Z | |
dc.date.available | 2023-12-05T13:49:02Z | |
dc.date.issued | 2023 | |
dc.department | Çankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümü | en_US |
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. | en_US |
dc.description.publishedMonth | 5 | |
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. | en_US |
dc.identifier.doi | 10.1016/j.rinp.2023.106405 | |
dc.identifier.issn | 22113797 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12416/6747 | |
dc.identifier.volume | 48 | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Results in Physics | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Bursting Bifurcation | en_US |
dc.subject | Discrete Fractional Operator | en_US |
dc.subject | Fractional Difference Equation | en_US |
dc.subject | Steady-States | en_US |
dc.subject | Synchronization | en_US |
dc.title | Dynamic prediction modelling and equilibrium stability of a fractional discrete biophysical neuron model | tr_TR |
dc.title | Dynamic Prediction Modelling and Equilibrium Stability of a Fractional Discrete Biophysical Neuron Model | en_US |
dc.type | Article | en_US |
dspace.entity.type | Publication |