Collaborative research: Computational models for this project will be developed in Dr. Bazhenov's laboratory (UC Riverside). Experimental work will be conducted in Dr. Stopfer's laboratory (NIH);Dr. Stopfer is serving as a co-Investigator for this proposal. Intellectual merit: A combined experimental-computational approach will be used to reveal mechanisms of odor coding in the insect olfactory system. The main goals of the study are: 1) To resolve, using new experimental techniques and modeling approaches, a long-standing question about the significance of synchrony and oscillation in the odor processing in insect olfaction;2) To clarify manifold and complex roles of inhibition in shaping responses of olfactory neurons. The locust and fly will be used as models for studying these different forms of inhibition in experiments involving simultaneous intracellular and multiunit recordings from Antennal Lobe (AL), Mushroom Bodies (MB) and Lateral Horn. The locust offers many advantages including the relative technical ease of making certain types of recordings. New genetic tools available only in the fly permit more detailed and specific tests of our hypotheses. Our group also has previously developed models of the locust AL and MB. Here we propose to extend these models to investigate how feedback and feed-forward inhibition may help to discriminate between similar odors, to achieve optimal odor representation across a range of concentrations, and to minimize the effects of input fluctuations. Although our existing models provide predictions and guidance, they do not yet converge with electrophysiological and behavioral data. Further, new experimental results from one of our laboratories (such as the discovery of odor-elicited oscillations in the fly AL and a reevaluation of the relative role of feedback and feed-forward inhibition in the locust MB) and other groups (such as the discovery of STDP in the locust MB) make necessary a critical re-evaluation of many previous hypotheses. We propose to achieve this with a set of carefully designed experiments to explore roles of transient oscillatory synchronization and spike coincidence detention in coding of different odors and different odor concentrations. Enhancing public health: At least 3,000,000 Americans suffer from chemosensory disorders;this number is likely to grow as the aging segment of the population increases. Olfactory impairment leads to an inability to detect hazards such as natural gas and spoiled food. Olfaction is also an important early signal of the onset of neurodegenerative diseases such as Parkinson's Disease. Broader impacts: Although the specific goal of this research proposal is to understand fundamental roles of inhibitory circuits in insect olfaction, research based on our models could be applied to study other sensory circuits, to understand and predict mechanisms responsible for oscillations and synchrony. This includes sleep rhythms and rhythmic epileptiform activity in the thalamocortical system, which have been intensively studied in Bazhenov's laboratory. One of our laboratories (Bazhenov) continuously involves undergraduate students (currently 4 students) to conduct research in the lab. This project will involve two minority undergraduate students - Martina Mikhail and Adam Jivraj - at the UCR. Martina has been working in the UCR PI lab since January 2011. She was recently awarded a summer fellowship from CAMP (California Alliance for Minority Participation) program. We expect other undergraduate students to be involved to this project as a part of their research training in the lab. Results from this new project will be included in an undergraduate lab class - Computational Neurobiology (CBNS 130L) - that is taught by the UCR PI. Furthermore, the UCR PI plans to develop a new graduate level interdisciplinary course "Computational Neuroscience of the Olfactory System" that would teach students by modeling design based on example of insect olfactory system. The relative simplicity of insect olfactory circuits allows presenting students with a complete model of olfactory processing, and to introduce fundamental ideas of synchrony and oscillations. All of the software for running the models will be made available online. UCR PI is involved in a new initiative of designing "Institute for the Application of Mathematics" at UCR that will provide cross-disciplinary training in mathematics and neuroscience. Results of this project will be used in such training. Bazhenov and postdocs employed on this award will be involved in outreach programs at UCR such as the UCR ALPHA Center (http://alphacenter.ucr.edu/mission.html) that is a facility for establishing long term engagements between faculty/campus personnel and K-12 education We will participate in the development of Web content related to this project that will become part of the ALPHA Center website. UCR participates in the Brain Awareness Week (BAW) program and we will continue this tradition by presenting results of this study during BAW events. Our other laboratory (Stopfer) regularly employs high school and college interns, and regularly provides public demonstrations of sensory science. This practice will be continued and the results of this project will be used in such events.

Public Health Relevance

These studies will help provide a scientific basis for treatment of the olfactory system disorders, such as anosmia and hyposmia, as well as promote understanding fundamental mechanisms of olfaction. In addition, this work will lead to insights into how pathways that contribute to olfactory information processing can be disrupted by the onset of Parkinson's Disease and other neurodegenerative disorders.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01DC012943-03
Application #
8676774
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Sullivan, Susan L
Project Start
Project End
Budget Start
Budget End
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of California Riverside
Department
Anatomy/Cell Biology
Type
Earth Sciences/Resources
DUNS #
City
Riverside
State
CA
Country
United States
Zip Code
92521
Bazhenov, Maxim; Huerta, Ramon; Smith, Brian H (2013) A computational framework for understanding decision making through integration of basic learning rules. J Neurosci 33:5686-97