Download or read book Genetic Dissection of the Neural Substrates for Behavior written by Xiaojing Gao and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In neuroscience, methodological advancements bring about new discoveries, while unanswered questions prompt technical innovations. My thesis involves both aspects, contributing to the genetic toolkit in fruit flies as well as our understanding of olfactory behavior. Innate olfactory attraction and aversion are observed throughout the animal kingdom, but it is not well understood how such valences are encoded by the sensory circuits, how the relevant behaviors are implemented, or, more fundementally, to what extent attraction and aversion share principles of information processing. Using state-of-the-art genetic tools, I demonstrate that aversion is much more robust than attraction against blockade of the sensory circuits (Chapter 2), and that aversion engages specific kinematic and motor-related neurons (Chapter 3). Aversion and attraction are thus likely processed by distinct circuits and principles throughout the sensory-motor transformation. In addition, Chapter 4 not only provides another case where attraction but not averson was affected by a genetic perturbation, but may also link a circuit for specific behavior to a gene necessary for the function of the circuit. To further our ability to explore neural circuits, I developed a transcriptional reporter of intracellular calcium (TRIC, Chapter 5). TRIC signals in the sensory systems depend on neuronal activity, and it sucessfully quantified neuronal responses that change slowly, such as those of neuropeptide F-expressing neurons to sexual deprivation and neuroendocrine pars intercerebralis cells to food and arousal. In the last case, I also demonstrate that TRIC can be used for circuit manipulation. TRIC can thus monitor neuromodulatory circuits whose activity varies slowly with the physiological states of the animal, and its modular design will facilitate future optimizations for even broader applications.