Spike-Timing-Dependent Plasticity and Short-Term Plasticity Jointly Control the Excitation of Hebbian Plasticity without Weight Constraints in Neural Networks

Figure 8

Distribution of the coefficient of variation (CV) at each synapse at Poisson inputs with 10 Hz. Each subfigure Figure 8 depicts the distribution of the CV at the given synapse of both neuron A and neuron B at Poisson inputs mean firing rate 10 Hz. For example, As shown in the leftmost subfigure shows the variation of CV of synapses from presynaptic neuron B to postsynaptic neuron A. Similarly, depicts the distribution of CV of synapses from presynaptic neuron A to postsynaptic neuron B. Moreover, the slopes of the mean release probability, and , were determined using linear regression analysis and give the slope of CV of from bin 1 to 150 and bin 150 to 200, respectively. Similarly, and give the CV of from bin 1 to bin 150 and from 150 to 200, respectively. As depicted in all these subfigures, at all the synapses when its around 200 bins, the difference of the CVs of neuron A and neuron B was about 0.001 and time functions of CVs of both neurons were paralleled to each other.