Alternating oscillatory and stochastic states in a network of spiking neurons

We focus on a phenomenon observed in cat visual cortex, namely the alternation of oscillatory and irregular neuronal activity. This aspect of the dynamics has been neglected in brain modelling, but it may be essential for the dynamic binding of different neuronal assemblies. The authors present a simple, but physiologically plausible model network which exhibits such a behaviour in spite of its simplicity—e.g. dendritic dynamics is neglected—as an emergent network property. It comprises a number of spiking neurons which are interconnected in a mutually excitatory way. Each neuron is stimulated by several stochastic spike trains. The resulting large input variance is shown to be important for the response properties of the network, which they characterize in terms of two parameters of the autocorrelation function: the frequency and the modulation amplitude. They calculate these parameters as functions of the internal coupling strength, the external input strength and several input connectivity schemes and distinguish parameter regions yielding irregular and periodic behaviour. In addition they find responses which alternate between the irregular and periodic time structure and which therefore reproduce the experimental findings very well. Important aspects of the stimulus dependence of the network response are in good agreement with experimental observations.