Scene analysis in the mammalian visual system, conceived as a distributed and parallel process, faces the so-called binding problem. As a possible solution, the temporal correlation hypothesis has been suggested and implemented in phase-coding models. We propose an alternative model that reproduces experimental findings of synchronized and desynchronized fast oscillations more closely. This model is based on technical considerations concerning improved pattern separation in associative memories on the one hand, and on known properties of the visual cortex on the other. It consists of two reciprocally connected areas, one corresponding to a peripheral visual area (P), the other a central association area (C). P implements the orientation-selective subsystem of the primary visual cortex, while C was modeled as an associative memory with connections formed by Hebbian learning of all assemblies corresponding to stimulus objects. Spiking neurons including habituation and correlated noise were incorporated as well as realistic synaptic delays. Three learned stimuli were presented simultaneously and correlation analysis was performed on spike recordings. Generally, we found two states of activity: (i) relatively slow and unordered oscillations at about 20-25 Hz, synchronized only within small regions; and (ii) faster and more precise oscillations around 50-60 Hz, synchronized over the whole simulated area. The neuron groups representing one stimulus tended to be simultaneously in either the slow or the fast state. At each particular time, only one assembly was found to be in the fast state. Activation of the three assemblies switched on a time scale of 100 ms. This can be interpreted as self-generated attention switching. On the time scale corresponding to gamma oscillations, cross correlations between local neuron groups were either modulated or flat. Modulated correlograms resulted if the groups coded features corresponding to a common object. Otherwise, the correlograms remained flat. This behavior is in agreement with experimental results, while phase-code models would generally predict modulated correlations also in the case of different objects. Furthermore, we derive a technical version from our biological associative memory model that accomplishes fast pattern separation parallel in O(log2 n) steps for n neurons and sparse coding.