Learning, Recognizing and Early Classification of Spatio-Temporal Patterns using Spike Timing Neural Networks
Learning and recognizing spatio-temporal patterns is an important problem for all biological systems. Gestures, movements, activities, all encompass both spatial and temporal information that is critical for implicit communication and learning. This dissertation presents a novel, unsupervised approach for learning, recognizing and early classifying spatio-temporal patterns using spiking neural networks for human-robotic domains. The proposed spiking approach has five variations which have been validated on images of handwritten digits and human hand gestures and motions. The main contributions of this work are as follows: i) it requires a very small number of training examples, ii) it enables early recognition from only partial information of the pattern, iii) it learns patterns in an unsupervised manner, iv) it accepts variable sized input patterns, v) it is invariant to scale and translation, vi) it can recognize patterns in real-time and, vii) it is suitable for human-robot interaction applications and has been successfully tested on a PR2 robot. This dissertation presents comparison between all variations of this approach with well-known supervised and unsupervised machine learning techniques on in-house and publicly available datasets. Although the proposed approaches in this dissertation are unsupervised, they outperform other state-of-the-art and in some cases, provide comparable results with other methods.
Early detection, Machine learning, Spike timing dependent plasticity, Spike timing neural network
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