Motivation is an internal drive that directs behavior towards a desired future state. To do so motivation affects how we make decisions and how we perceive the world. It also interacts with memory systems, so that learning and memory recall are more efficient under a relevant motivational state. While some motivations can be abstract and long-term, primary motivations such as hunger and thirst are essential for survival and exist even in the simplest animals. In human, defects in motivational systems have been linked to a variety of mental disorders, such as obsession, depression, eating disorder, and addiction.
Despite its clear importance, a complete biological description of how motivations are represented in the brain, how they control behaviour, and how they affect learning and memory remain to be established. To have such a description is no trivial exercise. It requires a detailed map of the neural networks that control motivated behavior and memory processing, as well as a comprehensive understanding of the algorithms and coding principles the networks use to compute and communicate.
My lab investigates these fundamental questions by studying hunger- and thirst-driven behavior in the fruit fly Drosophila melanogaster. Like us, the fruit fly seeks food when hungry and approaches water when thirsty. They can also learn and remember environmental cues that are associated with food and water, and later use this information to make decisions. The fruit fly achieves these complex behaviors with a nervous system consisting of only one hundred thousand neurons (there are one hundred billion in our brain). Powerful genetic techniques and the relative simplicity of the brain allow us to manipulate neural circuits with fine temporal precision at single-cell resolution. This provides a unique opportunity to comprehensively study how a brain processes and integrates multiple pieces of information, such as motivational states and learned experience, to select an appropriate course of action.
We will use a combination of genetics, two-photon calcium imaging, biochemistry, and quantitative behavior to establish causal links between neural network computation and behavior. Starting by exploiting the neural circuit of thirst and hunger, we hope to gain a better understanding on how brain stores, uses and integrates information received from the external world and information generated internally within the brain.
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