Foundations of a computational theory of catecholamine effects

This report presents the mathematical foundation of a theory of catecholamine effects upon human signal detection abilities. We argue that the performance-enhancing effects of catecholamines are a consequence of improved rejection of internal noise within the brain. To support this claim, we develop a neural network model of signal detection. In this model, the release of a catecholamine is treated as a change in the gain of a neuron's activation function. We prove three theorems about this model. The first asserts that in the case of a network that contains only one unit, changing its gain cannot improve the network's signal detection performance. The second shows that if the network contains enough units connected in parallel, and if their inputs satisfy certain conditions, then uniformly increasing the gain of all units does improve performance. The third says that in a network where the output of one unit is the input to another, under suitable assumptions about the presence of noise along this pathway, increasing the gain improves performance. We discuss the significance of these theorems, and the magnitude of the effects that they predict. This research was supported by the Defense Advanced Research Projects Agency (DOD) and monitored by the Space and Naval Warfare Systems Command under Contract N00039-87-C-0251, ARPA Order No. 5993 . The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of DARPA or the U.S. government