Brian H. Smith, PhD - US grants

This award provides funds to a group of neuroscientists at the University of Arizona for the purchase of a computer, networking hardware, and associated software. This equipment will be used to analyze experimental data resulting from comparative studies of olfaction and spatial orientation in lower and higher animals. Though collaborations with mathematicians, these investigators plan to generate models for nerve cell interactions that appear fundamental to these processes. Models will be based on anatomical and electrophysiological properties of the nervous system. Other models will have an explicitly behavioral basis. The models will developed using a neural network simulation program called GENESIS and the computer. Interspecific comparisons have traditionally provided a useful tool in understanding the underlying mechanisms of biological processes. Increasingly, the use of experimental data and theoretical schemes for the synthesis of models that make detailed predictions has played an equally important role in modern biology. Olfaction and spatial orientation are both problems of nervous integration that have interested neurobiologists for some time. The use of computational models, in particular neural network models, to get at the underlying integrative mechanisms is a promising approach to these classical problems.

[unreadable] DESCRIPTION (provided by applicant): This proposal will focus on using the honeybee (Apis mellifera) as a model animal species for understanding mechanisms of behavioral plasticity toward odors. These animals are faced with the same types of olfactory problems as mammals. They require the capability to detect and respond to a very large number of odors because they depend on locating many different types of flowers to harvest carbohydrate resources in nectars. Association of these odors with nectar changes many times within an individual's lifetime, which also requires that animals learn about these associations. Like mammals, insects exhibit a variety of means to learn about and encode memories for floral odors. Comparative evidence indicates that behavioral and systems-level neural processing similarities between insects and mammals have evolved independently. That suggests that there may only be one general neural solution available to encode information about odors. Therefore, as comparative models, insects can be critical for revealing how odors are learned and encoded in the CNS, and whether this mechanism is fundamental to olfactory processing. The aims of this proposal are to evaluate mechanisms of neural plasticity that exist in the insect Antennal Lobes (AL), which are the neural analogs to the mammalian Olfactory Bulb (Hildebrand & Shepherd 1997). The central theme is that documented neural mechanisms and modulatory pathways in the AL that represent the presence (or absence) of reinforcement serve as a means to filter out unimportant, variable background odors. This allows biologically relevant, learned odors to be readily detected. There will be three aims. First, behavioral investigations will examine in detail mechanisms that underlie a learned inattention (CS preexposure effect) toward odors and blocking in odor-odor mixtures. In particular, manipulations of the conditioning context will reveal the extent to which specific theoretical treatments of CS preexposure can account for it in insects. Second, multichannel recording techniques will be employed to investigate whether and how these behavioral mechanisms may be implemented as a filtering mechanism in the AL. Third, pharmacological and molecular manipulation by way of RNA interference will be used to examine how modulatory pathways represent reinforcement in the AL by regulating biogenic amine (serotonin and octopamine) release. [unreadable] [unreadable]

Studies outlined below will investigate the quantitative and molecular genetic bases for variation in learning performance in the honey bee, Apis mellifera. Research into the bases of learning and memory in invertebrates such as mollusks and insects has demonstrated that fundamental properties of behavioral plasticity developed in studies of vertebrates apply across a broad phylogenetic spectrum. Yet, the genetic bases for learning traits are only beginning to be studied. Honey bees provide excellent opportunities for studying genetic factors that influence learning behavior because of the potential to apply several powerful behavioral paradigms for studying learning in vertebrates. The series of proposed studies will take advantage of the ability to train and analyze honey bee drones (males). Drones arise from unfertilized eggs and are thus haploid recombinants of the maternal genotype. Quantitative genetic selection on a haploid proceeds faster than it would on a diploid. Furthermore, linkage mapping from haploid genotypes using a map recently developed from a series of RAPD primers will not have the complications of such mapping using diploid genotypes. The specific aims of the studies are: * To further determine the quantitative genetic bases underlying variability in olfactory learning performance. Lines will be selected for fast versus slow reversal learning performance relative to unselected control lines. Use of this conditioning protocol and inclusion of appropriate control procedures will allow us to test whether associative learning performance can be selected without correlated responses in sensitization or motor systems. * To investigate the molecular genetic background using a linkage map established from RAPD genetic markers. These kinds of analyses will be carried out in recombinant F2 drone progeny of hybrid queens, in which spurious correlations due to founder effects in small base populations would be attenuated.

DESCRIPTION (provided by applicant): Multiphoton excitation (MPE) microscopy has become a powerful optical tool in investigating neuronal structure and function in intact brain circuits. Arizona State University has recently made neuroscience a major research initiative in creating an interdisciplinary neuroscience program while hiring several new faculty members, who implement optical methods in their neuroscience research. Currently however, there are no MPE microscopes for conducting optical studies of neuronal structure and function in scattering tissues. We have developed this equipment grant composed of several NIH funded investigators in order to secure funds to purchase a MPE microscope. The acquisition of this equipment will permit the investigators in this proposal to become more competitive in continuing to secure NIH funding, as well as foster the growth of a couple junior investigators who have not yet secured NIH funding, but are undergoing the application and review processes. Further, the capabilities conferred by MPE microscopy will permit neuroscience investigators at ASU to continue conducting cutting-edge research using modern optical approaches. PUBLIC HEALTH RELEVANCE: We propose to purchase a multiphoton confocal microscope as a multi-user piece of equipment at Arizona State University. This type of high-end microscopy has revolutionized imaging in Neuroscience research applications since it was introduced over 15 years ago. This system will therefore augment the research capabilities of a large group of NIH-funded biomedical researchers at ASU.

The PIs and Co-PIs of grants supported through the NSF-NIH-ANR-BMBF-BSF Collaborative Research in Computational Neuroscience (CRCNS) program meet annually. This tenth meeting of CRCNS investigators brings together a broad spectrum of computational neuroscience researchers supported by the program, and includes poster presentations, talks, plenary lectures, and workshops. The meeting is scheduled for October 16-18, 2014 and is hosted by Arizona State University.

This project was developed during a NSF Ideas Lab on "Cracking the Olfactory Code" and is jointly funded by the Chemistry of Life Processes program in the Chemistry Division, the Mathematical Biology program in the Division of Mathematical Sciences, the Physics of Living Systems program in the Physics Division, the Neural Systems Cluster in the Division of Integrative Organismal Systems, the Division of Biological Infrastructure, and the Division of Emerging Frontiers. The sense of smell is essential for maintaining quality of life in humans, and its decline can be an important harbinger of neurodegenerative disease. Moreover, since nearly all animals aside from primates rely on olfaction for most survival functions, understanding chemical sensing has immense practical value, for example, in the control of agricultural pests or in training animals to detect odors relevant for bomb, drug and cancer detection. In spite of its importance, the understanding of olfaction lags far behind the other senses, which is in part due to the lack of understanding of the physical space of odors. The understanding of the neural bases of vision and audition were greatly advanced by investigations of the physical dimensions of visual and auditory stimuli. It is therefore likely that a similar in-depth investigation of odor space - how natural odors occur and the backgrounds against which they must be detected - will reveal a new depth of richness of neural representations of odors in the brain. Insects such as the fruit fly and honey bee are excellent models for this research because of the accessibility of their central nervous systems, because of their ease of use under controlled laboratory conditions, and because of the functional similarity of how odors are processed in insect and mammalian brains. This research will characterize how odor flowers and fruits with respect to behavioral value for honey bees (food) and fruit flies (food and egg laying sites). Further monitoring of neural activity in early and later stage processing in the brain, when combined with computational modeling, will reveal significantly richer neural representations than have heretofore been described. This new understanding stands to have an impact on understanding how healthy brains encode sensations and memories of odors and how brains fail under disease conditions. It will also have an impact on understanding how the sense of smell may be built into engineered devices. Finally, both insects are also of economic importance to agriculture for crop pollination (honey bees) and damage to fruit (fruit flies). The PIs will teach and work with undergraduate, graduate and postdoctoral students and especially recruit students from underrepresented groups in science.

This research will quantitatively characterize the real-world statistics of multi-component natural odor scenes and investigate how they drive behavior and processing in several brain regions. The focus will be on honey bee as well as fruit fly adults and larva as models, where it will be possible to characterize a library of ethologically relevant natural odors associated with a diversity of behavioral outputs. The work will begin by quantitatively characterizing the detailed statistical properties of natural odor scenes in defined ethological contexts. This will build on the rich literature on identified natural odors in insects and mammals. Naturally occurring plant and fruit odor samples from the natural environments of each insect will be collected and chemically analyzed. Nonlinear dimensionality reduction techniques and approaches based on sparse coding will determine the dimensions of odor space that are most salient for behavioral decisions. Such a quantitative deconstruction of the sensory input would be unprecedented in olfactory neuroscience, and should allow the PIs to effectively and comprehensively drive olfactory circuits for the first time. The hypothesis is that the stimulus dimensions that are most behaviorally relevant to the animal will be most efficiently extracted by the olfactory system. Synthetic odor blends will be specially constructed to vary along relevant sensory dimensions, to probe neural codes and adaptive behaviors in the olfactory system. As in research on the visual system, analysis of such evoked neural responses using statistical methods that take into account natural odor statistics will reveal novel olfactory computations and behaviors that have been previously inaccessible. The project will generate datasets of immediate use and importance to scientists in theoretical biology and mathematics, engineering and biology.

? DESCRIPTION (provided by applicant): Many animals, and particularly humans, depend on social networks for their general well-being and in many cases for their survival. The biomedical impacts of social networks on individuals can have important implications for regulating obesity and drug or alcohol abuse. The effects can also have a large impact on the functioning of any organization, ranging from small to large public and private organizations through military command and control. In many cases the central control of groups is by necessity loose or nonexistent, with the organization arising from sets of rules that each individual employs. It is therefore important to understand how adaptive, collective behavior emerges from a collection of individuals with little or no central control. The research in this proposal is aimed toward understanding group behavior by integrating models with experiments at three biological scales: from gene expression effects in neural networks of the brain, to how those networks affect behavior, to the collective dynamics of a coordinated group of individuals that operates without central control. We will investigate an important problem of group organization from a different perspective than is commonly used. Instead of having individuals who all operate under a common set of rules, as would be true of most agent-based approaches, we propose to study groups composed of individuals who vary in their behavioral rules. The latter condition is more typical of human and many animal groups; because individuals naturally differ in many ways - experience, size, age, etc. - that influence how they respond to various situations. We propose to develop the honey bee as an animal model for this type of work precisely because the survival of any individual in a large (ca 100,000 honey bees) social colony depends on the performance of the group as a whole which operates without central control. Moreover, we can study honey bee biology at multiple organizational scales. We can experimentally manipulate the expression of identified genes, monitor and manipulate neural networks in the brain, and determine the composition of honey bees of different genotypes in the colony. We will focus on how honey bee colonies solve a central problem in looking for food that humans also face. That is, how to allocate resources to exploiting a known resource versus exploring for new resources. Failure to efficiently perform both tasks by the several thousand foraging honey bees risks failure of the colony. We focus on a gene locus that has been repeatedly affiliated with one or another foraging specialty. We propose to investigate how different alleles at this locus influence the behavioral choices of individuals, and then investigate how those individuals are integrated into a colony's strategy for solving this foraging problem. We will use a novel multiscale modeling approach that integrates three biological scales using standard agent-based modeling, mean field approximations, decision making models of the brain, and gene regulatory models. Through a back-and-forth interplay between modeling and experiments, our approach will identify critical parameters that allow groups to face environmental challenges.

Animals develop an understanding of their environment through learning that specific cues in the environment are reliably paired with and consequently predict important outcomes, such as access to food or the presence of danger. The concept of outcome expectation based on these predictions has been influential in the development of studies of associative learning in mammals. These analyses have had a profound impact on understanding of outcome-related behavior in humans, including monetary rewards and the consequences of traumatic experiences, and for pathologies of the reward systems in the brain. The neural underpinnings of outcome expectation are particularly challenging to study in mammals because of the requirement for exquisite cellular, temporal, and genetic specificity of experimental manipulations. The fruit fly Drosophila melanogaster has been a valuable model for investigating the genetic and neural bases that underlie learning and memory, including learning of cue-outcome associations. The recent development of work with identified neurons and their detailed connections in the fly brain makes the larval and adult fruit fly brains ripe as models for advancing understanding of neural bases for outcome expectation learning in mammals. Using the powerful experimental approaches available in the fruit fly model system and resources guided by computational modeling, the team of researchers investigate complex memory representations in Drosophila. This project also provides interdisciplinary training for postdoctoral researchers, and graduate and undergraduate students, development of new K-12 biology classroom material, and collaboration with Arizona State University’s award-winning Ask-A-Biologist program.

Early and most current studies of learning and memory in the fruit fly (Drosophila melanogaster) use basic behavior conditioning protocols to study learning in controlled laboratory settings. The ability to transgenically manipulate many of the brain neurons in the fruit fly with exquisite specificity, and the recent knowledge of the synaptic ‘connectome’ of the fruit fly brain, makes these animals almost unique as a comprehensive model for studies of learning, memory and motivated behavior. Within this context, the investigators propose that studies of learning and memory will be greatly enhanced by using more sophisticated means for evaluating memory representations, and by combining those studies with information from the connectome guided by computational modelling. To that end, the investigators examine the function of reinforcement pathways in relation to the absence of expected reinforcement. Specifically, the investigators study the memory representations in fruit flies when an expected consequence of a conditioned stimulus fails to occur. Through a series of experiments, they test the prediction that in Drosophila when a conditioned stimulus is associated with a failed expectation of an appetitive food reinforcement, the conditioned stimulus will acquire aversive value, and vice versa for a failed expectation of an aversive reinforcer. The investigators combine these studies with manipulations of reinforcement pathways in the central nervous system identified and selected based on the recently released fly brain connectome. The experimental work is iteratively knitted in with established computational models.

A companion project is being funded by the French National Research Agency (ANR).

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.