Dopamine and Norepinephrine Modulation of Cortical and Subcortical Dynamics During Visuomotor Learning

Understanding the mechanisms of neuromodulation exerted by catecholamines over cortical and subcortical neurons has been one of the major challenges in neuroscience that remains unsolved. Although there is compelling neurophysiological evidence indicating that both dopamine (DA) and norepinephrine (NE) systems are critically involved in the control of neuronal functions, there are still many discrepancies on how these modulations occur, perhaps as result of the different experimental approaches used to investigate the effects. Of particular interest, experiments performed in behaving animals have provided significant amount of information about how these modulatory systems may regulate complex neural networks such as during behavioral conditions in which both excitatory and inhibitory neurons are engaged. In this regard, computational models could provide a better understanding on the neuronal dynamics underlying these interactions by formalizing biologically plausible hypotheses and predictions to be tested in realistic experimental conditions. Two main approaches have been successfully implemented to tackle this issue. The first one is bottom-up, that is, brain functions are modeled from neurophysiological bases, simulating the properties of neurons and synapses as close as possible. The other approach is top-down, that is, the behavioral and brain functions are simulated as close as possible to animal behavior. Unfortunately, both approaches suffer from the lack of experimental data to link neurons and higher brain functions. Because of this, it seems obvious to choose a position in between both approaches to start the modeling work. Behavioral results constrain the modeling process, giving top-down cues in the development of a model that incorporates molecular, physiological, and behavioral evidence parsimoniously. In this chapter, we will present and discuss how neural network models that take into account realistic biological data could be used to explain the roles of DA and NE underlying learning processes involved in simple and complex tasks performances.