A Stochastic Neural Model for Fast Identification of Spatiotemporal Sequences

In this Letter, a new approach to build a neural model for the fast identification of spatiotemporal sequences is proposed. Such a model, the Stochastic Neural Sequence Identifier (SNSI), is simple and rapidly learns and identifies a given sequence. The SNSI receives as input several patterns belonging to a particular spatiotemporal sequence and produces as output a label for the sequence identified and a probability of this classification being correct. The SNSI is able to identify a sequence from patterns learned during training or novel ones, i.e., combinations of the sequence items distinct from those belonging to the trained set. The SNSI was tested on a 2D set of both closed and open trajectories with varying levels of complexity. The results suggest that the SNSI is able to recognize all the patterns presented in the training and most of the novel patterns used for testing.