Study Of Effect Of Electromagnetic Field On Synchronization Of Neural Network

Synchronous oscillations are ubiquitous in neural systems, and it plays animportant role in realization of the function of the brain.Based on the modified HH model in external electrical field (EF), the effects ofDC and AC EF on the synchronous activities of the neural network are studied. In theanalysis of DC EF, the impact of EF and network parameters on the activity of theinhibitory network are investigated, the network synchronization is realized only in arelatively narrow gamma range of oscillation frequencies. It is shown that DC EF inspecific parameter range can weaken the negative effect of adaptation currentsintroduced in the excitatory population and reducing the inhibitory connectionsbetween inhibitory populations, which both can greatly suppress the coherence of theexcitatory-inhibitory network. In addition, it also can weaken the trend of coherencedecline in subnet due to stimulate competition. Different behaviors have beenobserved in the network with different topologies (random and small-world network,modular network). It is found that the coherence of the neuronal systems dependsextensively on the network structure. The effects of network parameters on thecoherence coefficient are also studied.In the analysis of AC EF, the inhibitory network shows high coherencecoefficient only when the neuron fires periodic spikes. The work in the thesis alsoshows that the effects of AC EF on the coherence coefficient and firing rate of thenetwork, with slower EF frequencies being more effective. The effects of differenttopologies (random and small-world network) on the network activities have noobvious difference, and the role of network parameters can only reflected in the localcontrol of the coherence coefficient and firing rate.Time delay generally exists in real neural network, so this dissertation is alsodedicated to study the relationship between the time-delay and the networksynchronization under the effect of DC and AC EF respectively. Taking thesmall-world network with electrical coupling and random networks with the couplingof the chemical synapses as the research objects, we analyze the influence law ofdifferent forms of time-delay constant and network parameters on the networksynchronization.In order to explore the underlying mechanisms supporting the interactionbetween the magnetic field (MF) and cortical network, we investigate the effects of MF and network parameters on synchronization of small-world and modular networkbased on Izhikevich model. Furthermore, the effects of MF parameters and learningtimes of STDP on the small-scale homogeneous and large-scale heterogeneousrandom network have been studied respectively, with introducing Spike Timingdependent Plasticity (STDP) of the synapse.The results could contribute to the scientific theoretical studies regarding theeffects of electromagnetic stimulation on human brain and motive furtherdevelopment of magnetic stimulation on treatment of neurological diseases.