The model's parameterisation is as of yet approximate, and physiological detail is incomplete, but these limitations are not relevant to the demonstrations we will report. The continuum formulation supplements the neural network approach, by enabling a different insight into the physical nature of synchrony, since the essential nonlinearity of individual elements is avoided, and the stochastic and essentially linear properties of the neuronal mass is emphasised. They observed synchrony at many levels, which they attributed largely to re-entrant activity at multiple levels in the pathway. Figures Figure 1 Essential properties of synchronous oscillation. A representation of the simulated cortical surface.
Open squares represent the sites of input of uncorrelated white noise.
The filled square is the reference point from which cross-correlations are calculated with respect to the rest of the field. The first and second principal eigenmodes of activity on the simulated cortical surface, obtained from the same data used to generate the top figures. Mean and SEM of cross-correlations from 20 1s simulation runs. In addition to the twin sites of asynchronous noise input to the same driving sites as in figure 1, spatio-temporally white noise has been input to all other sites in the field. Results are shown for two widely different conditions of non-specific cortical activation, as indicated by the value of parameter Q ns .