Richard C. Gerkin, PhD - US grants

[unreadable] DESCRIPTION (provided by applicant): Donald O. Hebb conceived of the cell assembly - a small network whose neurons fire in stereotyped sequences - to represent a memory trace. These cell assemblies are believed to underlie persistent activity patterns observed in many experimental preparations. However, it is unknown how these assemblies develop ex nihilo from a population of developing neurons, and how an assembly can retain stability once it emerges. The first of these questions bears directly on how memory traces can emerge in the brain. The second question is of direct relevance to epilepsy, a pathology characterized by unstable, hyperexcitable neuronal networks. Rat hippocampal culture provides a model system for studying these questions. In addition to being a highly tractable system, it supports the spontaneous development of reverberatory circuits, a hallmark of cell assemblies. Using this system in combination with perforated patch clamp recordings, I will examine the reciprocal relationship between activity-dependent synaptic plasticity and Hebbian cell assemblies. [unreadable] [unreadable]

Identification of the stimulus features represented by neuronal activity, and understanding the fidelity of this representation, are central to understanding how the brain processes the sensory world. In the mammalian olfactory bulb, mitral/tufted (M/T) cells both receive synaptic input from sensory neurons, and serve as the principal output to central brain structures. Due to physical constraints, odor intensity (concentration) at odorant receptors in vertebrates is likely to vary much more slowly than stimulus intensity in other sensory modalities such as vision or audition. Because of this speed limit, the precise timing of action potentials (APs) will not track rapid, millisecond-timescale changes in stimulus properties as in other sensory systems. Rather, the extra bandwidth afforded by precise AP timing could encode other olfactory stimulus features, such as identity or context. If odor identity is encoded in AP timing early during a stimulus response, and this timing is reliable across identical stimuli, behavioral response latencies could be minimized11. Across respiratory cycles, an identical ensemble AP response could serve to confirm odor identity. Alternatively, directed changes in this response across cycles could convey novel information in each respiratory cycle about a complex stimulus. Here I propose to test the hypotheses that ensemble AP timing encodes features of odor stimuli in the mouse olfactory bulb, and does so in a dynamic fashion across respiratory cycles.

? DESCRIPTION (provided by applicant): Theoretical neuroscientists use neuron models to predict, understand, and explain biological neuron behavior. They often work with reduced neuron models that abstract away biological details but capture essential neuronal dynamics. This choice facilitates mathematical tractability, conceptual analysis, and computational speed. However, the tradeoffs inherent in using such models (instead of biologically detailed ones) are not transparent. It is often unclear if a model is faithful to essential observed dynamics of the neuron, and if so, under what model parameters and stimulus conditions. It is also rare for multiple types of reduced models to be compared in this regard, making it difficult to select the most appropriate one for a scientific question. Lastly, such models, once developed and parameterized, usually are not shared among researchers in a form that facilitates reproducibility and re-use, nor can they be easily discovered. As a result, status quo behavior in the use of reduced models is often simply to choose a favorite model regardless of merit, to optimize it for the scientific question at hand, and then to discard it. A standard practice in professional software development is unit testing. A unit test is a procedure that validates a single component of a computer program against a single correctness criterion. An ongoing effort to develop an analogous unit-testing procedure for neuron models, NeuronUnit, enables the construction of validation tests-executable functions that validate models against a single empirical observation to produce a score indicating agreement between the model and an observation. NeuronUnit facilitates the construction and logical grouping of tests for neuron models, the parameterization of tests using a wide range of empirical data, and the execution of tests against models in a continuous and transparent fashion. Aggregate results provide both theoretical and experimental neuroscientists with an overview of model suitability for targeted research questions. Merits and deficiencies of competing models are clearly visible, benefiting ongoing modeling efforts and informing new theoretical and experimental directions. This proposal aims to expand NeuronUnit to create data-driven, neuron-type-specific validation tests for reduced models. The ability of a range of reduced models to capture the relevant membrane potential and spiking dynamics of specific biological neuron types in response to specific stimuli, using publicly available experimental data from numerous sources, will be quantitatively tested and visualized. In doing so, the merits and deficiencies of each reduced model-as well as tradeoffs in model complexity, speed, and analysis-become transparent, providing critical information for model choice. Project aims are to 1) express a large number of reduced models using NeuroML/LEMS, 2) implement NeuronUnit testing of these models against data from a wide range of neuron- and experiment-types, 3) provide web-based search and visualization for test results and corresponding simulations, and 4) make these models available both as NeuroML documents and as code for every NeuroML-supported simulator. Collaborations with multiple existing initiatives will promote uptake of these tools, which for the first time, will provide a rigorous, transparent process for evaluation and selection of reduced models to address scientific questions about neurons. This project goes beyond model sharing by facilitating the dissemination of information about the performance and applicability of reduced neuron models in the context of specific datasets, complementing the existing dissemination mode of manuscript publication. By making model choice more deliberate and model appropriateness more objective, this work highlights which models should be used to address which scientific questions and why, without the need for a deep literature search (for models and data) or the installation of new tools or re-coding of models for simulation. The project also serves neuroscience educators by providing an interactive platform for visualization of reduced model dynamics accessible to any student, using data from biological neurons. This work broadly transforms theoretical neuroscience: by giving modelers a tool to select models quickly and with clear purpose; by rigorously identifying the models best-suited for further research efforts; and by helping experimentalists enhance the impact of their work.

Psychophysics refers to a class of research methods and measurements used to study a perceptual system. In olfaction research, experimental psychophysics consists of behavioral tests that illuminate the relationship between odorants and the percepts they evoke. Theoretical olfactory psychophysics attempts to makes sense of these test results by constructing models that can predict or explain them. These models then constrain broader theories of olfaction (including neural mechanisms) and inform the design of experiments that probe those mechanisms. However, many potentially illuminating datasets from academia and industry that could inform or test theoretical efforts remain difficult to locate, access, and use. Models driven by previously available data have thus been only sparsely tested and are only weakly generalizable. Pyrfume is an effort to extensively curate data related to olfactory psychophysics, to transparently and automatically determine how well models make sense of this data, and to inform experimental design for olfactory research at large. It will consist of a central , research-focused database of human psychophysics research data extracted from literature, other disparate databases, and industrial sources. It will also contain complementary research data from animal models that directly address the same kinds of questions about specific stimuli, but at a neural level inaccessible to most human experiments. All of these data will be accessible via a common framework and be immediately usable for data-driven tests of competing models of olfaction in health and disease. RELEVANCE (See instructions): Smell is essential for social behavior, food enjoyment, and danger avoidance; anosmia is associated with depression, and olfactory decline is an early warning sign for neurodegenerative diseases. This project organizes understanding of human olfaction, facilitating quantification of olfactory health, disease biomarker development, and cures for olfactory disorders. Optometry and audiology are refined clinical tools that map perceptual deficits onto medical targets; this work can help advance olfactometry to the same level.

Biomedical research has experienced a crisis of reproducibility, exacerbated by lack of coordination between research groups, weak study designs, opaque algorithms, and an unwillingness to share the fruits of research efforts. But recent years have seen the beginning of a push to solve this crisis, with a focus on integration across labs, standardization, rigorous statistical analysis, open source code, and the sharing of data and methods. This proposal aims to ?Crack the Olfactory Code?, a far-reaching goal that will require a firm commitment to rigorous, open science principles. The Data Science Core (DSC) for this proposal reflects a plan to meet this commitment in three major ways: (1) The DSC will tightly coordinate the integration of data across the proposal?s five scientific projects, utilizing standards and adopting technology for total reproducibility of all findings, as well as ensuring that all research outputs will be made comprehensively and intelligibly available to the public; (2) The DSC will work with project leaders to optimize data collection decisions using statistical models, saving time and money while increasing scientific inference; (3) The DSC will standardize odorant stimuli across projects and create a service for pushing this standard into the larger research community, ensuring that future research efforts can be directly informed by our efforts and can inform each other.

The COVID-19 pandemic is the most devastating infectious disease outbreak in a century, particularly in underserved and minoritized communities. In 2020 alone, it will cost a million lives. It continues to wreak economic havoc worldwide. Therefore, it is critical to develop new tools that can mitigate the spread of SARS- CoV-2, the virus that causes COVID-19. Rapid screening tools can identify potentially infected individuals who can then be isolated/quarantined from the uninfected and directed towards further testing and treatment. Unfortunately, definitive viral testing for SARS-CoV-2 has proven difficult to implement in many countries, including the US, due to technical, financial and governmental hurdles to universal access and timely processing. Symptom-based screening offers a valuable, albeit imperfect, complement to viral testing that can help identify many individuals with the disease for isolation as well as treatment. A major challenge with symptomatic testing is that COVID-19 is highly protean: the heterogeneity of symptoms means no single symptom or constellation of symptoms is definitive diagnostically. Still, there is growing evidence that sudden partial or complete olfactory loss ? even more than other symptoms such as fever or dry cough ? is the single best predictor of COVID-19. In this proposal, we will develop and implement objective, self-administered smell tests for the purpose of identifying individuals with COVID-19 prior to, or in the absence of, viral testing, as well as for use in population-level surveillance of COVID-19 spread. Several kinds of objective tests have been used in clinical or laboratory settings to assess an individual's olfactory ability, including those that test the ability to identify or discriminate odors as well as procedures to determine the lowest concentration an individual can reliably perceive (i.e., odor detection threshold). Each approach has technical and logistical advantages and disadvantages, and each captures different aspects of olfactory dysfunction. Regarding COVID-19, it is unknown what type of measure has the highest specificity or sensitivity. In Aim 1, we will use self-administered objective testing of odor identification and odor detection threshold in SARS-CoV-2-tested individuals to determine which olfactory measure is the best predictor of COVID-19. In Aim 2, we will use objective smell testing to assess whether population monitoring of olfactory loss in university, municipal or other community settings can serve as a sentinel of COVID-19 community spread. Together, our studies will provide a rapid, remote-friendly, cost-effective, scalable, non-intrusive method to screen for COVID-19 at the individual level and to assess prevalence in communities, especially those that have been traditionally underserved by the health care system and public health infrastructure.

The sense of smell plays a central role in how people navigate many common workplace situations. Despite this, contemporary educational approaches have only recently begun to explore using olfaction to improve education, and research on multimedia learning has almost completely overlooked it. As researchers, policy makers, and educators continue to expand digital platforms for teaching and learning, the failure to understand the role played by situational cues such as smell threatens the effectiveness of implementing STEM learning in digital environments. This integrative transdisciplinary project draws together insights from engineering, computational neurosciences, neurobiology, teaching and learning sciences, and the social sciences to advance understanding of the role that olfaction plays in learning in virtual learning environments.<br/><br/>To investigate the role of olfaction into virtual learning environments, the project team will design software platforms that integrate control of hardware that delivers a physical olfactory stimulus into real-time 3D virtual environments. This project will advance learning and teaching technologies by: (i) developing portable hardware and software systems to reliably synthesize virtual odors through miniaturized physical apparatuses, (ii) designing principles of easy-to-use development tools for virtual odor space design, and (iii) developing cloud-powered systems to model complex odor propagation. This project aims to advance understanding of the role that explicit olfactory training plays in improving a learner’s ability to identify, localize, and describe odors. By exploring how incorporating odor affects cognitive and procedural learning and its impact on learning transfer for tasks related to olfactory identification, this research will expand understanding of multimedia learning theory. Insights from this research will be used to develop pedagogical teaching approaches for domains where olfaction is important and aligns with vocational skills. By infusing olfaction into virtual reality education spaces, this project aims to create broadly accessible education opportunities around overlooked sensory cues.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.