Character Recognition Using Hierarchical Vector Quantization and Temporal Pooling

In recent years, there has been a cross-fertilization of ideas between computational neuroscience models of the operation of the neocortex and artificial intelligence models of machine learning. Much of this work has focussed on the mammalian visual cortex, treating it as a hierarchically-structured pattern recognition machine that exploits statistical regularities in retinal input. It has further been proposed that the neocortex represents sensory information probabilistically, using some form of Bayesian inference to disambiguate noisy data. In the current paper, we focus on a particular model of the neocortex developed by Hawkins, known as hierarchical temporal memory (HTM). Our aim is to evaluate an important and recently implemented aspect of this model, namely its ability to represent temporal sequences of input within a hierarchically structured vector quantization algorithm. We test this temporal pooling feature of HTM on a benchmark of cursive handwriting recognition problems ...

• Type: Conference
• Year: 2008
• Where: AUSAI
• Authors: John Thornton, Jolon Faichney, Michael Blumenstein, Trevor Hine
• Keywords: Artificial Intelligence, AUSAI 2008, Temporal Pooling, Temporal Pooling Algorithm, Temporal Pooling Feature