There is now a need to develop novel integrative approaches that take into account the role of microglial inflammation, astrocytic processes, the BBB sink, and the interaction between every cell of the CNS, in order to develop efficient ways to target such complex pathologies as AD and MS. The Fonds de la Recherche du Québec – Santé (FRQS), Canadian Institutes in Health Research (CIHR), and the Multiple Sclerosis Scientific Research Foundation of Canada support this research. “
“Decision making is an abstract term referring Ku-0059436 molecular weight to the process of selecting a particular

option among a set of alternatives expected to produce different outcomes. Accordingly, it can be used to describe an extremely broad range of behaviors, ranging from various taxes of unicellular organisms to complex political behaviors in human society. Until recently, two different approaches have dominated the studies of decision making. On the one hand, a normative or prescriptive approach addresses the question of what is the best or optimal choice for a given type of decision-making problem. For example, the principle of utility maximization in economics and the concept of equilibrium in the game theory describe how self-interested rational agents should behave individually or in a group, respectively (von Neumann and Morgenstern, 1944). On the other hand, real behaviors of humans and animals

seldom match the predictions of such normative http://www.selleckchem.com/products/BIBW2992.html theories. Thus, empirical studies seek to identify a set of principles that can parsimoniously account for the actual choices

of humans and animals. For example, prospect theory (Kahneman and Tversky, 1979) can predict not only decisions of humans but also those of other animals more accurately than normative theories (Brosnan et al., 2007; Lakshminaryanan et al., 2008; Santos and Hughes, 2009). Similarly, empirical studies have demonstrated that humans often choose their behaviors altruistically and thus deviate from the predictions from the classical game theory (Camerer, 2003). Recently, these two traditional approaches of decision-making research have merged with two additional disciplines. First, it is now increasingly appreciated that learning plays an important role in decision Unoprostone making, although this has been ignored in most economic theories. In particular, reinforcement learning theory, originally rooted in psychological theories of learning in animals (Mackintosh, 1974) and optimal control theory (Bellman, 1957), provides a valuable framework to model how decision-making strategies are tuned by experience (Sutton and Barto, 1998). Second, and more importantly for the purpose of this review, researchers have begun to elucidate a number of important core mechanisms in the brain responsible for various computational steps of decision making and reinforcement learning (Wang, 2008; Kable and Glimcher, 2009; Lee et al., 2012).