Principles of Computational Modelling in Neuroscience

In this chapter, a range of models with fewer details than those in previous chapters is considered. These simplified neuron models are particularly useful for incorporating into networks, as they are computationally more efficient and sometimes they can be analysed mathematically. Reduced compartmental models can be derived from large compartmental models by lumping together compartments. Additionally, the number of gating variables can be reduced whilst retaining much of the dynamical flavour of a model. These approaches make it easier to analyse the function of the model using the mathematics of dynamical systems. In the yet simpler integrate-and-fire model, first introduced inand elaborated on in this chapter, there are no gating variables, with action potentials being produced when the membrane potential crosses a threshold. At the simplest end of the spectrum, rate-based models communicate via firing rates rather than via individual spikes.