Rachel I. Wilson - US grants

[unreadable] DESCRIPTION (provided by applicant): Odor molecules are sensed by olfactory receptor neurons, which in turn send information about odor stimuli to the olfactory bulb (in vertebrates), or the antennal lobe (in insects). All the receptor neurons that express the same olfactory receptor gene send information to the same discrete region (glomerulus) in the brain. What happens next-when olfactory information is processed by neural circuits in the brain-is still poorly understood. One difficulty is the complexity of the olfactory circuit: each glomerulus contains recurrent excitatory and inhibitory neural circuits, and receives lateral connections from other glomeruli. Drosophila is a good model system for investigating this problem, given the range of genetic tools available in the fruit fly. Also, the fly olfactory system is broadly similar to that of vertebrates, but much simpler. This study examines how olfactory information is processed by the circuitry of the antennal lobe. In particular, these experiments will dissect the odor-evoked electrophysiological response of second-order olfactory neurons in the antennal lobe (termed projection neurons, or PNs), using specific genetic manipulations that destroy or rescue function in the sensory inputs targeting single glomeruli. In vivo whole-cell patch-clamp recordings will be used to assess PN responses to olfactory stimulation of the fly's antennae. Specific aim #1 asks whether both inhibitory and excitatory synapses between glomeruli contribute to odor-evoked activity in PNs. Aim #2 tests the hypothesis that inhibitory and/or excitatory synapses between glomeruli are both stereotyped and specific. Aim #3 investigates the contribution of synaptic interactions within each glomerulus to the specific features of odor-evoked activity in PNs. This project should contribute substantially to our understanding of the very first steps of olfactory processing in the brain. Understanding early olfactory coding should help in treating olfactory disorders in human patients, and could aid in understanding why these disorders are often early warning signs of neurodegenerative diseases. Furthermore, understanding how the brain encodes odors has contributed valuable insights to the design of so-called "artificial noses", sensors designed to detect and discriminate between specific volatile chemicals. These sensors have important applications in medical diagnosis and biodefense, and have shown particular promise in diagnosing stage 1 lung cancer by measuring the chemicals present in a subject's breath [unreadable] [unreadable]

Summary This proposal addresses two fundamental questions: (1) How do neurons extract features of mechanosensory stimuli that are relevant for motor control? (2) How do central circuits create a flexible linkage between mechanosensory stimuli and behavior? These questions are relevant to human health because sensory processing and sensory-motor integration are disrupted in many neurological and psychiatric disorders. However, sensory processing and sensory-motor integration are not fully understood at the level of cellular mechanisms ? i.e., at the level of neural connectivity, cellular physiology, and synaptic physiology. This level of mechanistic explanation is important to understanding why disease-linked genes produce their characteristic phenotypes. It is also important to developing better therapeutics. As a model system for gaining mechanistic insight into these brain functions, this project will focus on the largest mechanosensory organ in Drosophila (Johnston's organ) and the circuits and behaviors downstream from this organ. Johnston's organ neurons (JONs) encode deflections of the distal antennal segment. These deflections can result from an object touching the antenna, wind, postural changes, or sound. In essence, therefore, Johnston's organ has a range of functions ? somatosensory, vestibular, and auditory. Different JON stimuli elicit different behaviors. These behaviors are variable and context-dependent (not stereotyped action patterns) and so we can use this system to study flexibility in sensory-motor coupling. Our first aim is to determine how JONs encode mechanical stimuli. To test the hypothesis that JONs are highly specialized for specific spatiotemporal features of antennal deflections, we will use a combination of in vivo calcium imaging, electrophysiology, and voltage imaging. Second, we will use in vivo whole cell recordings to test the hypothesis that central neurons postsynaptic to JONs can extract specific frequencies of antennal vibrations by virtue of their intrinsic electrical bandpass filtering characteristics. Third, we will perform in vivo whole cell recordings to investigate how mechanosensory signals are encoded at the level of third-order neurons, and how these signals are relayed to motor control centers. We hypothesize that wind and sound stimuli will be encoded by largely distinct neural channels. Fourth, we will combine whole-cell recording with simultaneous behavioral measurements to determine how mechanosensory cues from JONs steer walking direction in a context-dependent manner. We hypothesize that heading direction cues and context cues will converge at the level of descending motor control neurons that project to the ventral nerve cord. As a whole, this work will provide new insights into the neural computations that occur in mechanosensory processing and mechanosensory- motor integration, as well as the cellular mechanisms that implement these computations.

Summary Strategic behaviors often take account of uncertainty. For example, if we are presented with two conflicting pieces of information, we give less weight to the more uncertain source of information ? i.e., the source of information that leads to lower accuracy overall. Notably, even insects behave as if they make strategic use of their own uncertainty. Importantly, the neural correlates of uncertainty are essentially unknown. In this collaborative project, we will use modeling and neural imaging to identify the neural correlates of uncertainty. We will focus on the ?compass? in the Drosophila brain. The intrinsic neurons of the compass (EPG neurons) form a topographic map of heading direction. At any given moment, there is a ?bump? of neural activity in the EPG population which rotates like a compass needle as the fly turns. The position of the bump is influenced by internal self-motion cues, external visual cues, and external wind direction cues. In previous theoretical work, the EPG ensemble has been modeled as a ring attractor network. In general, ring attractors do not represent uncertainty in the variable they are encoding. Most experiments characterizing compass neuron activity have been performed either under conditions of extreme certainty (e.g., a bright visual cue), or extreme uncertainty (e.g., complete darkness). Therefore, it remains unclear how the system behaves under moderate uncertainty, and if, under such conditions, it can still be well-described by standard ring attractor networks. Ideally, the compass network would represent not only the fly's estimated heading direction, but also the uncertainty associated with that estimate, so that behavioral strategies could be adjusted accordingly. In this project, we will investigate (1) how uncertainty is represented, and (2) how it affects spatial learning. We will use a combination of algorithmic modeling, network modeling, and in vivo imaging experiments combined with virtual reality environments.