Standard artificial neural networks (ANNs), loosely based on the structure of columns in the cortex, model key cognitive aspects of brain function such as learning and classification, but do not model the affective (emotional) aspects of brain function. However these are a key feature of the brain (the associated `ascending systems' have been hard-wired into the brain by evolutionary processes) . These emotions are associated with memories when neuromodulators such as dopamine and noradrenaline affect  entire patterns of synaptically activated neurons. Here we present a bio-inspired ANN architecture which we call a Salience-Affected Neural Network (SANN), 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. During pattern recognition, inputs similar to the salience-affected inputs in the training data 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. Note that we are not aiming to present an accurate model of the biological salience system; rather we present an artificial neural network inspired by those biological systems in the human brain, that has unique strengths .
In this paper we introduce a novel Salience Affected Artificial Neural Network (SANN) that models the way neuromodulators such as dopamine and noradrenaline affect neural dynamics in the human brain by being distributed diffusely through neocortical regions. This allows one-time learning to take place through strengthening entire patterns of activation at one go. We present a model that accepts asalience signal, and returns areverse salience signal. We demonstrate that we can tag an image with salience with only a single training iteration, and that the same image will then produces the highest reverse salience signal during classification. We explore the effects of salience on learning via its effect on the activation functions of each node, as well as on the strength of weights in the network. We demonstrate that a salience signal improves classification accuracy of the specific image that was tagged with salience, as well as all images in the same class, while penalizing images in other classes. Results are validated using 5-fold validation testing on MNIST and Fashion MNIST datasets. This research serves as a proof of concept, and could be the first step towards introducing salience tagging into Deep Learning Networks and robotics .
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Standard artificial neural networks (ANNs), loosely based on the structure of columns in the cortex, model key cognitive aspects of brain function such as learning and classification, but do not model the affective (emotional) aspects of brain function. However these are a key feature of the brain (the associated `ascending systems' have been hard-wired into the brain by evolutionary processes) . These emotions are associated with memories when
In this paper we introduce a novel Salience Affected Artificial Neural Network (SANN) that models the way
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neural dynamics in the human brain by being distributed diffusely through neocortical regions. This allows one-time learning to take place through strengthening
R
synaptically activated neurons. Here we present a bio-inspired ANN architecture which we call a Salience-Affected Neural Network (SANN), 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. During pattern recognition, inputs similar to the salience-affected inputs in the training data 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. Note that we are not aiming to present an accurate model of the biological salience system; rather we present an artificial neural network inspired by those biological systems in the human brain, that has unique strengths
activation at one go. We present a model that accepts a
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salience signal
A
, and returns a
A
reverse salience signal
A
. We demonstrate that we can tag an image with salience with only a single training iteration, and that the same image will then produces the highest reverse salience signal during classification. We explore the effects of salience on learning via its effect on the activation functions of each node, as well as on the strength of weights in the network. We demonstrate that a salience signal improves classification accuracy of the specific image that was tagged with salience, as well as all images in the same class, while penalizing images in other classes. Results are validated using 5-fold validation testing on MNIST and Fashion MNIST datasets. This research serves as a proof of concept, and could be the first step towards introducing salience tagging into Deep Learning Networks and robotics