Multidimensional Hebbian Learning With Temporal Coding in Neocognitron Visual Recognition

Previous work on Fuskushima's neocognitron neural network model has mostly focused on binary character recognition, but grayscale images are preferable to binary images in real applications. Image classes of grayscale objects can be formed with an unsupervised learning process using the neocognitron neural network model. Since Hebbian learning as one that uses a time dependent and strongly interactive mechanism to increase synaptic efficiency as a correlation function between presynaptic and postsynaptic activity, and principal component analysis (PCA) is used in neuroscience extensively. An analogy is shown between unsupervised Hebbian learning and PCA when applied to the neocognitron model. The Hebbian learning theory is extended taking into consideration of temporal coding. Successful computer simulation models for grayscale object recognition are discussed. This paper is the first to carry out quantitative analysis of the neuron responses for gray scale image classification.