One-time learning and reverse salience signal with a salience affected neural network (SANN)
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Standard artificial neural networks model key cognitive aspects of brain function, such as learning and classification, but they do not model the affective (emotional) aspects; however primary and secondary emotions play a key role in interactions with the physical, ecological, and social environment. These emotions are associated with memories when neuromodulators such as dopamine and noradrenaline affect entire patterns of synaptically activated neurons. Standard artificial neural networks (ANNs) do not model this non-local effect of neuromodulators, which are a significant feature in the brain (the associated `ascending systems' have been hard-wired into the brain by evolutionary processes). In this paper we present a salience-affected neural network (SANN) model which, at the same time as local network processing of task-specific information, includes non-local salience (significance) effects responding to an input salience signal. We show that during training, a SANN allows for single-exposure learning of an image and associated salience signal. During pattern recognition, input combinations similar to the salience-affected inputs in the training data sets will produce reverse salience signals corresponding to those experienced when the memories were laid down. In addition, training with salience affects the weights of connections between nodes, and improves the overall accuracy of a classification of images similar to the salience-tagged input after just a single iteration of training.
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