I constructed a cortical neural network model and investigated possible roles of coherent ongoing oscillations in membrane potentials of neurons in object perception. The model has a hierarchical structure consisting of two lower networks and one higher network that are reciprocally connected via divergent/convergent projections. Information about features and their relationships (or objects) is encoded by the population activities of neurons (or dynamic cell assemblies) of the lower networks and the higher network, respectively. The ongoing state of the network is expressed by 'random itinerancy' among these dynamic cell assemblies. Under the ongoing state, the dynamic cell assemblies belonging to the same object are transiently linked across the networks and coherently oscillate at lower frequencies (approximately 15 Hz). When the model perceives a presented object, the dynamic cell assemblies corresponding to the object are persistently linked together across the networks and coherently oscillate at higher frequencies (approximately 40 Hz). When the feedback pathways are impaired, the dynamic phase transition from the slow- to fast-oscillations is not induced by the object presentation, keeping the lower frequency oscillations (approximately 15 Hz) where the activated dynamic cell assemblies oscillate incoherently. Reaction times to the object presentation are greatly reduced if the ongoing oscillation frequencies fall within a specific range (approximately 20-30 Hz). I suggest that coherent ongoing slow-oscillations in cortical activity may serve as a ready state for sensory input, whereby the brain can respond effectively to sensory stimulation. Top-down processing via feedback pathways may give an essential contribution to the induction of coherent fast-oscillations across multiple cortical areas, by which relevant features are effectively integrated into a unified percept when stimulated with a sensory object.