  By controlling the state of neuronal populations, neuromodulators ultimately
affect behaviour. A key neuromodulation mechanism is the alteration of neuronal
excitability via the modulation of ion channel expression. This type of
neuromodulation is normally studied via conductance-based models, but those
models are computationally challenging for large-scale network simulations
needed in population studies. This paper studies the modulation properties of
the Multi-Quadratic Integrate-and-Fire (MQIF) model, a generalisation of the
classical Quadratic Integrate-and-Fire (QIF) model. The model is shown to
combine the computational economy of integrate-and-fire modelling and the
physiological interpretability of conductance-based modelling. It is therefore
a good candidate for affordable computational studies of neuromodulation in
large networks.
