Among the predictions that can be derived from the phase-coding model is the phase modulation of spikes in the cortex in relationship to stimulus or behavioral manipulations. We earlier argued that reconstruction takes place in the supragranular layer of the neocortex. According to our model, layers 2–3 and 4b pyramidal cells vigorously respond to the granule cell input only if the time of input APs coincides with the cell's intracellular SMO peaks. In our simulations the optimal coincidence time window was ∼1 ms (Nadasdy, 2009). Empirically, however, this time window is a probability function, rather than a binary function, allowing neurons to fire less frequently when the input is away from the peak but still reaches threshold. When the stimulus is optimal for the neuron, the AP will be generated reliably near the intracellular SMO peak (LFP trough). The same neuron may also respond, although less likely, to a suboptimal stimulus. If the suboptimal stimulus is optimal for another neuron, it will drive that neuron at the exact intracellular SMO peak. However, due to the slight phase difference between the two intracellular SMO processes, the same depolarization that drives the other neuron at exact SMO peak will drive the first neuron at a slightly different SMO phase than would its own optimal stimulus. As a result we shall observe a modest phase difference between spikes of the same neuron when we vary the stimulus parameters within the receptive field. Studies are in progress to test this prediction. Prefrontal cortical neurons in a working memory task exhibit memory item dependent phase offset relative to the slow oscillations (Siegel et al., 2009). Other studies investigating the auditory and visual cortex found feature-dependent phase differences relative to theta in auditory (Kayser et al., 2009) and relative to alpha in primary visual cortex (Montemurro et al., 2008) and to gamma (Nadasdy and Andersen, 2009) also in primary visual cortex. It is also conceivable that the phases of local SMOs shift relative to the LFP, which integrates oscillations over a larger cell population (Harvey et al., 2009). We anticipate an increasing amount of data to arise in support of these so-far isolated examples in cortical recordings.