Modeling Neuronal Assemblies: Theory and Implementation

Models that describe qualitatively and quantitatively the activity of entire groups of spiking neurons are becoming increasingly important for biologically realistic large-scale network simulations. At the systems and areas modeling level, it is necessary to switch the basic descriptional level from single spiking neurons to neuronal assemblies. In this article, we present and review work that allows a macroscopic description of the assembly activity. We show that such macroscopic models can be used to reproduce in a quantitatively exact manner the joint activity of groups of spike-response or integrate-and-fire neurons. We also show that integral as well as differential equation models of neuronal assemblies can be understood within a single framework, which allows a comparison with the commonly used assembly-averaged graded-response type of models. The presented framework thus enables the large-scale neural network modeler to implement networks using computational units beyond the single spiking neuron without losing much biological accuracy. This article explains the theoretical background as well as the capabilities and the implementation details of the assembly approach.