Robust Character Recognition Using a Hierarchical Bayesian Network

There is increasing evidence to suggest that the neocortex of the mammalian brain does not consist of a collection of specialised and dedicated cortical architectures, but instead possesses a fairly uniform, hierarchically organised structure. As Mountcastle has observed [1], this uniformity implies that the same general computational processes are performed across the entire neocortex, even though different regions are known to play different functional roles. Building on this evidence, Hawkins has proposed a top-down model of neocortical operation [2], taking it to be a kind of pattern recognition machine, storing invariant representations of neural input sequences in hierarchical memory structures that both predict sensory input and control behaviour. The first partial proof of concept of Hawkins' model was recently developed using a hierarchically organised Bayesian network that was tested on a simple pattern recognition problem [3]. In the current study we extend Hawkins'...

• Type: Conference
• Year: 2006
• Where: AUSAI
• Authors: John Thornton, Torbjorn Gustafsson, Michael Blumenstein, Trevor Hine
• Keywords: Artificial Intelligence, AUSAI 2006, Bayesian Network, Hierarchical Bayesian Network, Neural Network