Efficient Coding of Time-Relative Structure Using Spikes
Neural Computation
A primitive based generative model to infer timing information in unpartitioned handwriting data
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
| Hi-index | 0.00 |
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.