def METHOD_NAME(neuron_model_name: str, synapse_model_name: str, fname_snip: str = ""):
    fname_snip += "_[neuron=" + neuron_model_name + "]"
    fname_snip += "_[synapse=" + synapse_model_name + "]"
    sim_time = 1000.  # [ms]
    pre_spike_time = 100.  # sim_time / 2  # [ms]
    delay = 10.   # [ms]
    # plot
    if TEST_PLOTS:
        fig, ax = plt.subplots()
    dt_vec = []
    dw_vec = []
    for post_spike_time in np.linspace(25, 175, 31) - delay:
        dt, dw = run_stdp_network(pre_spike_time, post_spike_time,
                                  neuron_model_name,
                                  synapse_model_name,
                                  resolution=1.,  # [ms]
                                  delay=delay,  # [ms]
                                  sim_time=sim_time,  # if None, computed from pre and post spike times
                                  custom_synapse_properties={"lambda": 1E-6, "alpha": 1.})
        dt_vec.append(dt)
        dw_vec.append(dw)
    # plot
    if TEST_PLOTS:
        ax.scatter(dt_vec, dw_vec)
        ax.set_xlabel(r"t_post - t_pre")
        ax.set_ylabel(r"$\Delta w$")
        for _ax in [ax]:
            _ax.grid(which="major", axis="both")
            _ax.grid(which="minor", axis="x", linestyle=":", alpha=.4)
        fig.suptitle("Neuron model: " + str(neuron_model_name.split("__with_")[0]))
        fig.savefig("/tmp/stdp_synapse_test" + fname_snip + "_window.png", dpi=300)