Predicting synchronized neural assemblies from experimentally estimated phase-resetting curves

Neural-network dynamics frequently organize in assemblies of synchronized neurons that are thought to encode and store sensory information. We have investigated the mechanisms leading to the emergence of these neural assemblies with models of coupled oscillators. In particular, we used experimentally estimated phase-resetting curves (PRC) of real neurons (mitral cells) to realistically describe neurons as phase oscillators. Our simulations revealed that the formation of synchronized assemblies is a rather general phenomenon. In fact, a mathematical analysis proved that a necessary condition for synchronized assemblies to occur is that the Fourier expansion of the PRC contains a positive sine.