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(α,Δφ)  The dependence of the stationary phase difference Δφ (denoted as "p") on the synaptic rate α is shown. The red lines show the stable phase differences, and the blue lines show the unstable phase differences. The form of lines changes according to the neuron models. By clicking the field, you can change the value of synaptic rate α. (The pink line show the present value of α.) By clicking the outside of the field, the pink line disappears. 

V(φ), Z(φ), I(φ)  The membrane potential V(φ) of the neuron, the phase response Z(φ), and the synaptic input I(φ) are shown. The waveform of I(φ) changes according to the value of the synaptic rate α. You can regard the phase φ as the time t. 

Γ(φ), Γ_{odd}(φ)  Γ(φ) and Γ_{odd}(φ) calculated from Z(φ) and I(φ) are shown. The intersections of Γ_{odd}(φ) and the horizontal axis show the stationary phase differences Δφ, and the red circles and the blue circles show the stable and unstable phase differences, respectively. This graph corresponds to the graph in (α,Δφ) plane. 

Simulation of the network with 100 neurons.  When the synchronized solution (Δ&phi=0) is stable, all neurons tend to synchronize with each other. 
We treat two oscillatin neurons interacting with weak couplings.
For simplicity, let us define the dynamics of one neuron as
where f(x) governs the dynamic of neuron x, and I(x,x') is the synaptic input from the other neuron. The synaptic inputs are often modeled by a sum of exponential functions or alpha functions. where t_{k} is the kth firing time of this neuron and α is the synaptic rate. Generally, weakly coupled two oscillators can be reduced to the phase model written as where Z(φ) is the phase response function that shows the change in frequency caused by a small perturbation. After averaging, the dynamics of weakly coupled two neurons follow the phase model written as where Γ(φ) can be calculated by the following formula. The dynamics of the phase difference Δφ of two oscillating neurons can be analyzed by investigating the following model. Note that the stationary phase difference Δφ satisfies Γ_{odd}(Δφ)=0, and its stability can be determined by the sign of the slope. You can perform analyses of synchronization in pulse neural networks using the phase response function. You can choose various neuron models and synaptic inputs.
When the stationary phase difference with Δφ=0 is stable, neurons tend to fire synchronously. The definitions of neuron models are shown in this page. 