Resonance And Synchronization In Neural Networks With Hybrid Synapses

The brain has complex neuronal network structure. The physiological functionsof memory, recognition, emotion and behavior are accomplished by reception,integration, transmission and output of the neuronal signal. The neuronal informationprocessing in the brain is a result of the cooperation of different brain regions.Neurons are coupled through chemical and electrical synapses to form neuronalnetworks, resonance and synchronization are important mechanisms of informationcommunication in neuronal network. It is find that synchronous exception is the mainexpression of some brain function diseases and the research of synchronizationbehavior can provide a basis for the treatment of brain diseases. The study ofresonance can be helpful to the detection and propagation of the subthreshold signal.A neuronal network with small-world topology is firstly constructed coupled withhybrid of chemical and electrical synapses based on the FitzHugh-Nagumo neuronmodel to investigate its vibrational resonance phenomenon. The simulation resultsshow that there exists an optimal amplitude of high-frequency stimulation, by whichthe dynamical response of excitable neuronal systems to a sunthreshold signal reachesthe peak. For the same input, the resonant effect of neuronal systems dependsextensively on the network structure and parameters. Chemical synapses have moreefficiency due to its selected coupling and neuronal networks with hybrid synapsesare beneficial for the detection and propagation of subthreshold signal.To simulate the mechanism of synchronization in different brain functionmodular, a modular neuronal network with small-world sub-networks is constructedbased on the Rulkov discrete neuron model. The neurons in this network are coupledin a hybrid excitatory/inhibitory way. It is found that the topology and parameter ofthe neuronal network exert an influence on the synchronization behavior, i.e. theincrease of the coupling strength in the sub-network will favor the synchronizationwhile as the coupling strength between the sub-networks increases, thesynchronization will be inhibited. Especially, the variation of the time delay caninduce intermittent transmission of synchronization in neuronal networks with hybridsynapses. The ratio of excitatory/inhibitory synapses in the neuronal network is also an important parameter which can influence the synchronization intermittent: forsmaller and larger probability of inhibitory synapses, intermittent synchronizationtransition is relatively profound, while for the moderate probability of inhibitorysynapses, synchronization transition seems less profound.The presented results based on the small-world and modular neuronal networkwith hybrid synapses reveals the effect of network topology and parameter on theresonance and synchronization and could have implications for the understanding themechanisms of neural information detection and processing.