Neuronal mechanism of state control in Drosophila melanogaster

The changes in internal states, such as fear, hunger and sleep affect behavioral responses in animals. In most of the cases, these state-dependent influences are “pleiotropic”: one state affects multiple sensory modalities and behaviors; “scalable”: the strengths and choices of such modulations differ depending on the imminence of demands; and “persistent”: once the state is switched on the effects last even after the internal demands are off. These prominent features of state-control enable animals to adjust their behavioral responses depending on their internal demands. Here, we studied the neuronal mechanisms of state-controls by investigating energy-deprived state (hunger state) and social-deprived state of fruit flies, Drosophila melanogaster, as prototypic models. To approach these questions, we developed two novel methods: a genetically based method to map sites of neuromodulation in the brain and optogenetic tools in Drosophila.

These methods, and genetic perturbations, reveal that the effect of hunger to alter behavioral sensitivity to gustatory cues is mediate by two distinct neuromodulatory pathways. The neuropeptide F (NPF) – dopamine (DA) pathway increases sugar sensitivity under mild starvation, while the adipokinetic hormone (AKH)- short neuropeptide F (sNPF) pathway decreases bitter sensitivity under severe starvation. These two pathways are recruited under different levels of energy demands without any cross interaction. Effects of both of the pathways are mediated by modulation of the gustatory sensory neurons, which reinforce the concept that sensory neurons constitute an important locus for state-dependent control of behaviors. Our data suggests that multiple independent neuromodulatory pathways are underlying pleiotropic and scalable effects of the hunger state.

In addition, using optogenetic tool, we show that the neural control of male courtship song can be separated into probabilistic/biasing, and deterministic/command-like components. The former, but not the latter, neurons are subject to functional modulation by social experience, supporting the idea that they constitute a locus of state-dependent influence. Interestingly, moreover, brief activation of the former, but not the latter, neurons trigger persistent behavioral response for more than 10 min. Altogether, these findings and new tools described in this dissertation offer new entry points for future researchers to understand the neuronal mechanism of state control.

Stability of Hypervelocity Boundary Layers

The early stage of laminar-turbulent transition in a hypervelocity boundary layer is studied using a combination of modal linear stability analysis, transient growth analysis, and direct numerical simulation. Modal stability analysis is used to clarify the behavior of first and second mode instabilities on flat plates and sharp cones for a wide range of high enthalpy flow conditions relevant to experiments in impulse facilities. Vibrational nonequilibrium is included in this analysis, its influence on the stability properties is investigated, and simple models for predicting when it is important are described.

Transient growth analysis is used to determine the optimal initial conditions that lead to the largest possible energy amplification within the flow. Such analysis is performed for both spatially and temporally evolving disturbances. The analysis again targets flows that have large stagnation enthalpy, such as those found in shock tunnels, expansion tubes, and atmospheric flight at high Mach numbers, and clarifies the effects of Mach number and wall temperature on the amplification achieved. Direct comparisons between modal and non-modal growth are made to determine the relative importance of these mechanisms under different flow regimes.

Conventional stability analysis employs the assumption that disturbances evolve with either a fixed frequency (spatial analysis) or a fixed wavenumber (temporal analysis). Direct numerical simulations are employed to relax these assumptions and investigate the downstream propagation of wave packets that are localized in space and time, and hence contain a distribution of frequencies and wavenumbers. Such wave packets are commonly observed in experiments and hence their amplification is highly relevant to boundary layer transition prediction. It is demonstrated that such localized wave packets experience much less growth than is predicted by spatial stability analysis, and therefore it is essential that the bandwidth of localized noise sources that excite the instability be taken into account in making transition estimates. A simple model based on linear stability theory is also developed which yields comparable results with an enormous reduction in computational expense. This enables the amplification of finite-width wave packets to be taken into account in transition prediction.