In computational neuroscience, the<unk>  describes the dynamics of interactions between populations of very simple excitatory and inhibitory model neurons. It was developed by Hugh R.<unk>  and Jack D.<unk>  and extensions of the model have been widely used in modeling neuronal populations. The model is important historically because it uses phase plane methods and numerical solutions to describe the responses of neuronal populations to stimuli. Because the model neurons are simple, only elementary limit cycle behavior, i.e. neural oscillations, and stimulus-dependent evoked responses are predicted. The key findings include the existence of multiple stable states, and hysteresis, in the population response.
Wilson–Cowan model
Wells-Riley model
Reed–Frost model
Krogh model
Mentzer index
Kokko and Rector Model
Pan–Tompkins algorithm
Donabedian model