Abstract. 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' work by comparing the performance of a backpropagation neural network with our own implementation of a hierarchical Bayesian network in the well-studied domain of character recognition. The results show that even a simplistic implementation of Hawkins' model can produce recognition rates that exceed a standard neural network approach. Such results create a strong case for the further investigation and development of Hawkins' neocortically-inspired approach to building intelligent systems. Key words: Bayesian network, cerebral cortex, character recognition 1 The Hierarchical Bayesian Network Figure 1 presents a simplified example of the hierarchical Bayesian network used in the current study. This network is based on George and Hawkins' original implementation [3] and consequently does not follow standard Bayesian update procedures, as the paper will explain. The network input consists of a feature vector of 100 real-valued elements, with a value range from 0 to 1. The particular form of the input was set by the feature extraction method used in the neural network implementation (described in Section 2). We retained this feature vector for the Bayesian network to ensure that our results reflect differences between the two computational architectures and are not confounded by effects from differing representations of the problem domain.
Based on this previous work, an independent implementation of George's and Hawkins' hierarchical Bayesian network was created in REF , in which its performance was compared to a back propagation neural network applied to a character recognition problem.
Robust Character Recognition using a Hierarchical Bayesian Network
ICANN
Computer Science