Ying-Mei Qin

Vibrational resonance in feedforward network

This paper investigates vibrational resonance in multi-layer feedforward network (FFN) based on FitzHugh-Nagumo (FHN) neuron model. High-frequency stimuli can improve the input-output linearity of firing rates, especially for the inputs with low firing rate. For FFN network, it is found that high-frequency disturbances play important roles in enhancing the propagation of weak signal through layers. Synfire-enhanced phenomenon of signal propagation is also observed in multi-layers network, when the signal transmission is affected by high-frequency disturbances. Network connections are found to be important for the propagation of weak signal. Besides that, the characteristics of high-frequency stimuli such as heterogeneity and frequency can also modulate the propagation of neural code through layers.

Propagation of spiking regularity and double coherence resonance in feedforward networks

We investigate the propagation of spiking regularity in noisy feedforward networks (FFNs) based on FitzHugh-Nagumo neuron model systematically. It is found that noise could modulate the transmission of firing rate and spiking regularity. Noise-induced synchronization and synfire-enhanced coherence resonance are also observed when signals propagate in noisy multilayer networks. It is interesting that double coherence resonance (DCR) with the combination of synaptic input correlation and noise intensity is finally attained after the processing layer by layer in FFNs. Furthermore, inhibitory connections also play essential roles in shaping DCR phenomena. Several properties of the neuronal network such as noise intensity, correlation of synaptic inputs, and inhibitory connections can serve as control parameters in modulating both rate coding and the order of temporal coding.

The effects of external electrical field on a neural network with synaptic plasticity and conduction delays

Effects of external DC stimuli to the evolution of a neural network model are studied. Its found that the external DC stimuli can enhance the neural networks average firing rate, induces the neural network to get to a balanced state that exhibits rhythmic activity with high rate. The activity of the neural network can be affected by the synaptic connection strength, the conduction delays, the external stimuli and so on. These parameters can serve as factors in modulating the evolution of the neural network with different firing activities. Furthermore, these results may also relate to the mechanism of memory and learning process.

Spiking patterns of hippocampus networks in DC electrical field

Effects of electrical field on CA3 hippocampus network are investigated based on the novel models of CA3 neuron and neuronal network in electrical field we developed in our study. Firing patterns of single neuron in different DC electrical field are investigated. Its found that firing activities are sensitive to the amplitude of the DC electrical fields, which may imply the role of endogenous electrical field in modulating brain activities. In network scale, spatiotemporal behaviors, especially synchronization, are studied in DC electrical field. Application of electrical field can change the synchronous states of the CA3 network. Electrical field and the non-synaptic connection may have effects on the generation and propagation of epileptiform activities. Furthermore, control strategies based on electrical field may be developed to inhibit the epileptiform activities.