Introduction to the theory of neural computation
Introduction to the theory of neural computation
Self-organizing maps
Speech enhancement based on neural predictive hidden Markov model
Signal Processing
Unsupervised learning
Self-Organizing Maps and Learning Vector Quantization forFeature Sequences
Neural Processing Letters
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A global optimization technique for statistical classifier design
IEEE Transactions on Signal Processing
On-line learning algorithms for locally recurrent neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Learning continuous trajectories in recurrent neural networks with time-dependent weights
IEEE Transactions on Neural Networks
Context in temporal sequence processing: a self-organizing approach and its application to robotics
IEEE Transactions on Neural Networks
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
A stochastic neural model for fast classification of binary images
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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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.