I propose a neural network model for intersensory facilitation and investigate its essential neuronal mechanisms. The model consists of sensory networks (SI, SII) and an integration network (IT). The integration network binds information derived from the sensory networks and sends feedback signals to them. Through a Hebbian learning process, point attractors representing individual features and objects are created in the dynamics of the sensory networks and integration network, respectively. The ongoing state of each network is a randomly itinerant state among these point attractors. When the SI network is stimulated with a feature (I_ n ) belonging to an object (O_ n ) with suprathreshold intensity, the point attractor corresponding to I_ n emerges in the SI network, but does not when stimulated with subthreshold intensity. Intersensory facilitation occurs when associate feature II_ n derived from the other modality belonging to the same object (O_ n ) is simultaneously presented to other sensory network SII, where the point attractor corresponding to feature I_ n emerges even when the sensory networks are stimulated with subthreshold intensity. I suggest here that the dynamic interaction of relevant point attractors across multiple neural networks is essential for intersensory facilitation, and that self-organized synaptic modulation effectively contributes to intersensory facilitation when crossmodal stimuli are separated in time.