bioRxiv preprint doi: https://doi.org/10.1101/726331; this version posted August 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. One-time learning and reverse salience signals with a Salience Affected Neural Network (SANN) Leendert A Remmelzwaal1* Y ¤, George F R Ellis2 Y ¤, Jonathan Tapson3 Y 1 Department of Electrical Engineering, University of Cape Town, Cape Town, Western Cape, South Africa 2 Department of Mathematics and Applied Mathematics, University of Cape Town, Cape Town, Western Cape, South Africa 3 MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia Y All three authors contributed equally to the first stage of this work [66] ¤ These authors equally took the lead in the further development presented here. * Corresponding author:
[email protected] Abstract 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.