ALTERNATING OSCILLATORY AND STOCHASTIC STATES IN A NETWORK OF SPIKINGNEURONS

We focus on a phenomenon observed in cat visual cortex, namely the alt ernation of oscillatory and irregular neuronal activity. This aspect o f the dynamics has been neglected in brain modelling, but it may be es sential for the dynamic binding of different neuronal assemblies. We p resent a simple, but physiologically plausible model network which exh ibits such a behaviour in spite of its simplicity-e.g. dendritic dynam ics is neglected-as an emergent network property. It comprises a numbe r of spiking neurons which are interconnected in a mutually excitatory way. Each neuron is stimulated by several stochastic spike trains. Th e resulting large input variance is shown to be important for the resp onse properties of the network, which we characterize in terms of two parameters of the autocorrelation function: the frequency and the modu lation amplitude. We calculate these parameters as functions of the in ternal coupling strength, the external input strength and several inpu t connectivity schemes and distinguish parameter regions yielding irre gular and periodic behaviour. In addition we find responses which alte rnate between the irregular and periodic time structure and which ther efore reproduce the experimental findings very well. Important aspects of the stimulus dependence of the network response are in good agreem ent with experimental observations.