Novelty estimation in developmental networks: Acetylcholine and norepinephrine

The receiver operating characteristic (ROC) curve has been widely applied to classifiers to show how the threshold value for acceptance changes the true positive rate and the false positive rate of the detection jointly. However, it is largely unknown how a biological brain autonomously selects a confidence value for each detection case. In the reported work, we investigated this issue based on the class of Developmental Networks (DNs) which have a power of abstraction similar to symbolic finite automata (FA) but all the DN's representations are emergent (i.e., numeric from the physical world and non-symbolic). Our theory is based on two types of neurotransmitters: Acetylcholine (Ach) and Norepinephrine (NE). Inspired by studies that proposed Ach and NE represent uncertainty and unpredicted uncertainty, respectively, we model how a DN uses Ach and NE to allow neurons to collectively decide acceptance or rejection by estimated novelty based on past experience, instead of using a single threshold value. This is a neural network, distributed, incremental, automatic version of ROC.