How well do oscillator models capture the behaviour of biological neurons?

It has been proposed that groups of neurons firing synchronously provide a mechanism that underlies many cognitive functions such as attention, associative learning, and working memory, as well as opening up communication channels between neuron groups. A mathematical abstraction that is gaining increasing acceptance for modeling neural information processing is the Kuramoto oscillator model, which can be used as an elementary unit to represent populations of oscillatory neurons. Whilst the Kuramoto model is widely used to capture fundamental properties of the collective dynamics of interacting communities of oscillatory neurons, the question arises as to how well it performs this role. This paper aims to address that question experimentally by using neural models to replicate the most fundamental of Kuramoto's findings, in which he showed that for any number of oscillators there is a critical coupling value Kc below which the oscillators are fully unsynchronized and another critical coupling value KL ≥ Kc above wich all oscillators become fully sunchronized. In this study, we replace Kuramoto oscillators with oscillating polulations both of quadratic integrate-and-fire neurons and of Hodgkin-Huxley neurons to establish whether Kuramoto's findings still hold in a more biologically realistic setup. The individual oscillators use a pyramidal inter-neuronal gamma architecture designed using a novel evolutionary technique.