Motion detection in video streams is a challenging task for several computer vision applications. Indeed, segmentation of moving and static elements in the scene allows to increase the efficiency of several challenging tasks, such as human-computer interface, robot visions, and intelligent surveillance systems. In this paper, we approach motion detection through a multi-layered artificial neural network, which is able to build for each background pixel a multi-modal color distribution evolving over time through self-organization. According to the winner-take-all rule, each layer of the network models an independent state of the background scene, in response to external disturbing conditions, such as illumination variations, moving backgrounds, and jittering. As a result, our background subtraction method exhibits high generalization capabilities that in combination with a post-processing filtering schema allow to produce accurate motion segmentation. Moreover, we propose an approach to detect anomalous events (such as camera motion) that require background model re-initialization. We describe our method in full details and we compare it against the most recent background subtraction approaches. Experimental results for video sequences from the 2012 and 2014 CVPR Change Detection data sets demonstrate how our methodology outperforms many state-of-the-art methods in terms of detection rate.