def METHOD_NAME(model):
    """
    Test linear summation of input for ps models with two time constants.
    The test creates three neurons n1, ..., n3 and connects each to a
    `spike_generator` such that:
    * n1 receives one excitatory spike
    * n2 receives one inhibitory spike at the same time
    * n3 receives both spikes
    We let :math:`V_0 = E_L` be the membrane potential at :math:`t_0`
    (same for all) and :math:`V_1`, :math:`V_2`, :math:`V_3` be the potentials
    of n1, n2, n3 after simulation. Then, we expect due to linearity
    .. math::
        ( V_1 - V_0 ) + ( V_2 - V_0 ) = V_3 - V_0
    """
    nest.set(resolution=0.25, tics_per_ms=1024.0)
    nest.SetDefaults(model, {"tau_syn_ex": 5.0, "tau_syn_in": 1.0})
    t_spike1 = 2.375  # 2.5 - 0.125 offset
    w1 = 1000.0
    sg = nest.Create("spike_generator", {"precise_times": True, "spike_times": [t_spike1]})
    n1 = nest.Create(model)
    n2 = nest.Create(model)
    n3 = nest.Create(model)
    # excitatory connection only
    nest.Connect(sg, n1, "one_to_one", {"weight": w1})
    # inhibitory connection only
    nest.Connect(sg, n2, "one_to_one", {"weight": -w1})
    # excitatory and inhibitory connection
    nest.Connect(sg, n3, "one_to_one", {"weight": w1})
    nest.Connect(sg, n3, "one_to_one", {"weight": -w1})
    # assume that neurons are initialized at equilibrium
    V0 = n1.V_m
    # sanity check for test
    assert V0 == n1.E_L
    nest.Simulate(4.0)
    V1 = n1.V_m
    V2 = n2.V_m
    V3 = n3.V_m
    assert (V1 - V0) + (V2 - V0) == V3 - V0