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Drosophila Reward Circuits  

John S. Hernandez, Tariq M. Brown, and Karla R. Kaun

The ability to sense and respond to a rewarding stimulus is a key advantage for animals in their natural environment. The circuits that mediate these responses are complex, and it has been difficult to identify the fundamental principles of reward structure and function. However, the well-characterized brain anatomy and sophisticated neurogenetic tools in Drosophila melanogaster make the fly an ideal model to understand the mechanisms through which reward is encoded. Drosophila find food, water, intoxicating substances, and social acts rewarding. Basic monoaminergic neurotransmitters, including dopamine (DA), serotonin (5-HT), and octopamine (OA), play a central role in encoding these rewards. DA is central to sensing, encoding, responding, and predicting reward, whereas 5-HT and OA carry information about the environment that influences DA circuit activity. In contrast, slower-acting neuromodulators such as hormones and neuropeptides play a key role in both encoding the pleasurable stimulus and modulating how the internal environment of the fly influences reward sensation and seeking. Recurring circuit motifs for reward signaling identified in Drosophila suggest that these key principles will help elucidate understanding of how reward circuits function in all animals.


Models of Decision-Making Over Time  

Paul Cisek and David Thura

Making a good decision often takes time, and in general, taking more time improves the chances of making the right choice. During the past several decades, the process of making decisions in time has been described through a class of models in which sensory evidence about choices is accumulated until the total evidence for one of the choices reaches some threshold, at which point commitment is made and movement initiated. Thus, if sensory evidence is weak (and noise in the signal increases the probability of an error), then it takes longer to reach that threshold than if sensory evidence is strong (thus helping filter out the noise). Crucially, the setting of the threshold can be increased to emphasize accuracy or lowered to emphasize speed. Such accumulation-to-bound models have been highly successful in explaining behavior in a very wide range of tasks, from perceptual discrimination to deliberative thinking, and in providing a mechanistic explanation for the observation that neural activity during decision-making tends to build up over time. However, like any model, they have limitations, and recent studies have motivated several important modifications to their basic assumptions. In particular, recent theoretical and experimental work suggests that the process of accumulation favors novel evidence, that the threshold decrease over time, and that the result yields improved decision-making in real, natural situations.