$x_2$, and their sum $x_\Sigma=x_1+x_2$, respectively. Other parameters: $a=1.05$, $\epsilon_1=0.005$, $\epsilon_2=0.1$, $\tau=0$, $C=0.07$ and $D_2=0.09$.](fig03a "fig:"){width="0.5\linewidth"} ![\[fig1\](a): Noise-induced oscillations in a single FitzHugh-Nagumo system ($C=0$) for a noise intensity $D_1=0.6$. The red dashed and dash-dotted lines denote the null-isoclines. Panel (b): Time series of two instantaneously coupled FitzHugh-Nagumo systems for different noise intensities $D_1$. The green (light grey), red (dark grey), and black curves correspond to $x_1$, $x_2$, and their sum $x_\Sigma=x_1+x_2$, respectively. Other parameters: $a=1.05$, $\epsilon_1=0.005$, $\epsilon_2=0.1$, $\tau=0$, $C=0.07$ and $D_2=0.09$.](fig03b "fig:"){width="0.44\linewidth"}

Figure \[fig1\](a) depicts the temporal dynamics of a single FitzHugh-Nagumo system ($C=0)$ due to stochastic input. One can see that the system is excitable since it performs large excursions in phase space. Figure \[fig1\](b) shows the temporal dynamics of the two instantaneously coupled neural systems for increasing noise intensities $D_1$, where the green (light grey), red (dark grey), and black curves correspond to $x_1$, $x_2$, and their sum $x_\Sigma=x_1+x_2$, respectively. For $D_1=0$, the first subsystem is enslaved and emits a spike every time the second unit does. For increasing $D_1$, however, this synchronization is weakened and for large noise intensities $D_1$ the dynamics of the first subsystem is independently dominated by its own stochastic input.

There are 