@inproceedings {PUBA173,
	title = {Generating Motion Trajectories by Sparse Activation of Learned Motion Primitives},
	author = {Christian Vollmer AND Julian Eggert AND Horst-Michael Gro{\ss}},
	year = {2012},
	month = {September},
	abstract = {We interpret biological motion trajectories as composed of sequences of sub-blocks or motion primitives. Such primitives, together with the information, when they occur during a motion, provide a compact representation of movement.We present a two-layer model for movement generation, where the higher level consists of a number of spiking neurons that trigger motion primitives in the lower level. Given a set of handwritten character trajectories, we learn motion primitives, together with the timing information, with a variant of shift-NMF that is able to cope with large data sets. From the timing information for a class of characters, we then learn a generative model based on a stochastic Integrate-and-Fire neuron model. We show that we can generate good reconstructions of characters with shared primitives for all characters
modeled.},
	publisher = {ICANN},
	booktitle = {Int. Conf. on Artificial Neural Networks (ICANN)},
	volume = {1},
	pages = {637-644}
}