This study investigates the bifurcation dynamics underlying rhythmic transitions in a biophysical hippocampal–cortical neural network model.We specifically focus on the membrane potential dynamics of excitatory neuro...This study investigates the bifurcation dynamics underlying rhythmic transitions in a biophysical hippocampal–cortical neural network model.We specifically focus on the membrane potential dynamics of excitatory neurons in the hippocampal CA3 region and examine how strong coupling parameters modulate memory consolidation processes.Employing bifurcation analysis,we systematically characterize the model's complex dynamical behaviors.Subsequently,a characteristic waveform recognition algorithm enables precise feature extraction and automated detection of hippocampal sharp-wave ripples(SWRs).Our results demonstrate that neuronal rhythms exhibit a propensity for abrupt transitions near bifurcation points,facilitating the emergence of SWRs.Critically,temporal rhythmic analysis reveals that the occurrence of a bifurcation is not always sufficient for SWR formation.By integrating one-parameter bifurcation analysis with extremum analysis,we demonstrate that large-amplitude membrane potential oscillations near bifurcation points are highly conducive to SWR generation.This research elucidates the mechanistic link between changes in neuronal self-connection parameters and the evolution of rhythmic characteristics,providing deeper insights into the role of dynamical behavior in memory consolidation.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12272002 and 12372061)the R&D Program of Beijing Municipal Education Commission(Grant No.KM202310009004)+1 种基金the North China University of Technology(Grant No.2023XN075-01)the Youth Research Special Project of the North China University of Technology(Grant No.2025NCUTYRSP051)。
文摘This study investigates the bifurcation dynamics underlying rhythmic transitions in a biophysical hippocampal–cortical neural network model.We specifically focus on the membrane potential dynamics of excitatory neurons in the hippocampal CA3 region and examine how strong coupling parameters modulate memory consolidation processes.Employing bifurcation analysis,we systematically characterize the model's complex dynamical behaviors.Subsequently,a characteristic waveform recognition algorithm enables precise feature extraction and automated detection of hippocampal sharp-wave ripples(SWRs).Our results demonstrate that neuronal rhythms exhibit a propensity for abrupt transitions near bifurcation points,facilitating the emergence of SWRs.Critically,temporal rhythmic analysis reveals that the occurrence of a bifurcation is not always sufficient for SWR formation.By integrating one-parameter bifurcation analysis with extremum analysis,we demonstrate that large-amplitude membrane potential oscillations near bifurcation points are highly conducive to SWR generation.This research elucidates the mechanistic link between changes in neuronal self-connection parameters and the evolution of rhythmic characteristics,providing deeper insights into the role of dynamical behavior in memory consolidation.
