Coupling Relationships between the Brain and the Central Pattern Generator Based on a Fractional-Order Extended Hindmarsh-Rose Model.
central nervous system
central pattern generator
critical state
Journal
Journal of integrative neuroscience
ISSN: 0219-6352
Titre abrégé: J Integr Neurosci
Pays: Singapore
ID NLM: 101156357
Informations de publication
Date de publication:
10 May 2024
10 May 2024
Historique:
received:
29
09
2023
revised:
13
12
2023
accepted:
28
12
2023
medline:
30
5
2024
pubmed:
30
5
2024
entrez:
30
5
2024
Statut:
ppublish
Résumé
The states of the central nervous system (CNS) can be classified into subcritical, critical, and supercritical states that endow the system with information capacity, transmission capabilities, and dynamic range. A further investigation of the relationship between the CNS and the central pattern generators (CPG) is warranted to provide insight into the mechanisms that govern the locomotion system. In this study, we established a fractional-order CPG model based on an extended Hindmarsh-Rose model with time delay. A CNS model was further established using a recurrent excitation-inhibition neuronal network. Coupling between these CNS and CPG models was then explored, demonstrating a potential means by which oscillations generated by a neural network respond to periodic stimuli. These simulations yielded two key sets of findings. First, frequency sliding was observed when the CPG was sent to the CNS in the subcritical, critical, and supercritical states with different external stimulus and fractional-order index values, indicating that frequency sliding regulates brain function on multiple spatiotemporal scales when the CPG and CNS are coupled together. The main frequency range for these simulations was observed in the gamma band. Second, with increasing external inputs the coherence index for the CNS decreases, demonstrating that strong external inputs introduce neuronal stochasticity. Neural network synchronization is then reduced, triggering irregular neuronal firing. Together these results provide novel insight into the potential mechanisms that may underlie the locomotion system.
Sections du résumé
BACKGROUND
BACKGROUND
The states of the central nervous system (CNS) can be classified into subcritical, critical, and supercritical states that endow the system with information capacity, transmission capabilities, and dynamic range. A further investigation of the relationship between the CNS and the central pattern generators (CPG) is warranted to provide insight into the mechanisms that govern the locomotion system.
METHODS
METHODS
In this study, we established a fractional-order CPG model based on an extended Hindmarsh-Rose model with time delay. A CNS model was further established using a recurrent excitation-inhibition neuronal network. Coupling between these CNS and CPG models was then explored, demonstrating a potential means by which oscillations generated by a neural network respond to periodic stimuli.
RESULTS AND CONCLUSIONS
CONCLUSIONS
These simulations yielded two key sets of findings. First, frequency sliding was observed when the CPG was sent to the CNS in the subcritical, critical, and supercritical states with different external stimulus and fractional-order index values, indicating that frequency sliding regulates brain function on multiple spatiotemporal scales when the CPG and CNS are coupled together. The main frequency range for these simulations was observed in the gamma band. Second, with increasing external inputs the coherence index for the CNS decreases, demonstrating that strong external inputs introduce neuronal stochasticity. Neural network synchronization is then reduced, triggering irregular neuronal firing. Together these results provide novel insight into the potential mechanisms that may underlie the locomotion system.
Identifiants
pubmed: 38812382
pii: S0219-6352(23)00698-8
doi: 10.31083/j.jin2305096
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
96Subventions
Organisme : Shandong Provincial Natural Science Foundation
ID : ZR2022MF340
Informations de copyright
© 2024 The Author(s). Published by IMR Press.
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.