We present in this paper some results on the temporal segmentation and retrieval of stored memories or patterns using neural networks composed of spiking neurons. Respecting the working environment，we present the network with stochastic or chaotic stimuli as their extremely working conditions and also with noise. We attempt to give an explanation to the function of memory retrieval of the brain system, where the stimuli usually may not be constant, sinusoidal or periodic, but rather chaotic or stochastic. For an input pattern which is a superposition of several stored patterns, it is shown that the proposed neuronal network model is capable of segmenting out each pattern one after another as synchronous firings of a subgroup of neurons，and if a corrupted input pattern is presented, the network is shown to be able to retrieve the perfect one，that is it has the function of associative memory. By thorougly adjusting the parameters, such as the coupling strength and the intensity of the noise, the temporal segmentation attains its optimal performance at intermediate noise intensity，which reminds of the stochastic resonance observed in the coupled spiking neuronal networks.