Takaki Komiyama, Ph.D. - US grants

DESCRIPTION (provided by applicant): The sense of smell plays an important role in our quality of life. However, the mechanisms governing the processing and representation of olfactory information in the brain are not well understood. In mammals, the olfactory bulb is a critical brain region responsible for the initial processing of olfactory information. Much of our current understanding of olfactory bulb function is based on previous studies using in vitro brain slices or acute preparations of anesthetized animals. While these approaches have provided valuable insight, much less is known about odor coding in awake, behaving animals. For example, what are the dynamics of odor representations in the same animal over long time scales (i.e. days, weeks, months) and how is odor coding in the olfactory bulb shaped by experience and learning in awake, behaving animals? To address these questions, we propose an experimental strategy using chronic 2-photon calcium imaging to study odor-evoked activity in the olfactory bulbs of awake mice. Specific Aim 1 proposes the development of an approach for the selective expression of the genetically-encoded calcium indicator GCaMP3 in principal (mitral/tufted) cells or local interneurons using a Cre-dependent, adeno-associated virus system. We hypothesize that this approach will provide single cell resolution of action potential-dependent calcium signals in large populations of neural ensembles that can be imaged chronically in awake, head fixed mice. Specific Aim 2 proposes imaging experiments to determine how odor-evoked responses of mitral cells and interneurons (granule cells) differ between the awake and anesthetized state. We will also use chronic imaging in awake animals to determine whether patterns of odor-evoked activity in neural ensembles are stable or dynamic over days of repeated testing. These experiments will provide new insight into the nature of odor coding in the awake brain.

Learning requires the conversion of transient experiences into long-lasting changes in neural circuitry. Animal behavior triggers changes in gene expression in small populations of neurons and behaviorally induced genes regulate synapses and neuronal morphology. Yet, it is unclear if changes in gene expression are the cause of behavioral plasticity, or the consequence. This project will develop a new genre of fluorescent reporters that enable the visualization and manipulation of endogenous transcription factors in individual neurons, in real time, and within the brain of behaving animals. During the award period, candidate reporters will be made that recognize six different transcription factors. These reporters will have widespread utility for investigating the molecular mechanisms that support learning in vivo and analysis of populations of neurons that are active during a learning paradigm. The development of these reporters includes ongoing training of undergraduate, graduate, and postgraduate scientists. Student training is optimized with guidance from the CREATE STEM Success Initiative on the UCSD campus.

Inducible transcription factors (ITFs) translate signals that last milliseconds or seconds into changes in cellular function that may persist for hours, days, or longer. This project will develop genetically encoded transcription factor reporters (GETFaRs) that are designed to visualize or manipulate an ITF. GETFaRs are based on molecular scaffolds, engineered through a process of synthetic affinity maturation of camelid nanobodies (Nbs) which bind the endogenous ITF. The Nb protein will be fused to a fluorophore or DNA modifying enzyme, allowing users to visualize or manipulate endogenous transcription factors. A degradation signal (degron) will be incorporated into the Nb near the ITF binding site. Consequently, GETFaRs will be constitutively expressed and rapidly degraded in the cytoplasm. When the ITF is expressed, the GETFaR-ITF interaction will mask the degron, stabilizing the complex. The ITF's nuclear localization signal will translocate the complex into the nucleus, resulting in stabilized GETFaRs that accumulate in the nucleus and stoichiometrically reflect ITF expression. Candidate GETFaRs will be validated in vitro using standard biochemical and imaging techniques and in vivo using two photon imaging of neurons in head fixed mice. Optimal GETFaRs will enable research that 1) monitors or manipulates transcriptional states during learning, 2) studies the emergence of ensembles of co-active neurons within a circuit, 3) probes the dynamics of chromatin and nuclear organization, and 4) analyzes the genome of defined populations of neurons responding to complex, natural stimuli.

? DESCRIPTION (provided by applicant): Motor learning is a fundamental form of learning important for the well-being of many animal species including humans. The importance of learned motor programs is underscored when they are compromised in motor disorders such as multiple sclerosis and ALS. Neural circuit mechanisms of motor learning have been extensively studied; however, precise plasticity mechanisms of distinct neuron types underlying motor learning are not well understood. We address this issue in the motor cortex, a critical brain region responsible for motor learning. The central hypothesis in this proposal is that subtype-specific changes of inhibitory neurons regulate the plasticity of excitatory circuits necessary for motor learning. To directly visualize these plasticity events within the motor cortex during motor learning, we will apply in vivo two-photon imaging chronically in awake mice performing a motor learning task over weeks, focusing on three major neuron types in the motor cortex (principal excitatory neurons, parvalbumin-expressing inhibitory neurons (PV-INs), and somatostatin-expressing inhibitory neurons (SOM-INs)). We recently developed a lever-press task as a motor learning paradigm for head-fixed mice. We found that learning of this task over two weeks induces a novel and reproducible activity pattern in motor cortex excitatory neuron ensembles. This activity change coincided with a turnover of dendritic spines, the major postsynaptic sites of excitatory synapses, on the excitatory neurons (Peters et al. Nature 2014). Following up on these initial findings, this proposal aims to reveal the role of inhibitory circuits in regulating he plasticity of excitatory circuits. In Aims 1&2, we will characterize the activity and synapse number of PV- and SOM-INs during learning. We hypothesize that motor learning transiently increases PV inhibition and decreases SOM inhibition. We will test this hypothesis by chronically imaging the activity of PV- and SOM-INs using GCaMP6f and their axonal presynaptic terminals. In Aim 3, we will test the hypothesis that the decrease in SOM inhibition is important for excitatory synaptic plasticity and learning. We will test this by manipulating SOM-IN activity using optogenetics and examine the effect on learning and dendritic spine turnover. Finally in Aim 4, we will develop additional motor learning paradigms for head-fixed mice, which will be combined with above experiments in the future to test how generalizable our findings on plasticity mechanisms are to various tasks. These experiments combine cutting-edge technologies including chronic high-resolution two-photon imaging, behavioral tasks by head-fixed mice, mouse genetics to label specific neuron types and optogenetics. These experiments will reveal fine-scale circuit plasticity underlying motor learning and also establish paradigm that can be applied to other forms of learning and behaviors in the future.

? DESCRIPTION (provided by applicant): Through repetitive experience, one can better detect or discriminate sensory stimuli. This form of learning, termed perceptual learning, fundamentally shapes the way our brains process sensory information. The precise loci and mechanisms underlying perceptual learning are still debated. We address mechanisms of olfactory perceptual learning, focusing on the olfactory bulb, the first olfactory center of the brain. In th olfactory bulb, newborn inhibitory neurons are continuously integrated throughout adulthood, providing a remarkable potential for plasticity. Our central hypotheses are that 1) olfactory perceptual learning improves odor discriminability by the ensemble activity of mitral cells, the principal neurons in the olfactory bulb, and that 2) adult-born inhibitory neurons show particularly adaptive plasticity and support mitral cell plasticity during learning. To address thee ideas, we will apply in vivo two-photon calcium imaging chronically in the olfactory bulb of awake mice undergoing various olfactory experience paradigms over days. This is combined with mouse genetics to specifically label mitral cells and adult-born inhibitory neurons and ablate adult neurogenesis. We recently developed a system to chronically image the activity of defined populations of neurons in the olfactory bulb of awake mice (Kato et al. Neuron 2012, Kato et al. Neuron 2013). The current proposal extends this approach to characterize the dynamics of odor representations in the olfactory bulb during one-week-long experience paradigms. In Aim 1, we will characterize mitral cell responses to a pair of very similar odors during one-week-long experience in a passive exposure condition as well as several discrimination learning tasks. Our preliminary results suggest that discrimination learning enhances the discriminability of experienced odors by mitral cell ensemble activity. In Aim 2, we will test whether adult neurogenesis is necessary for olfactory perceptual learning and mitral cell plasticity. We will use genetic strategies to block adult neurogenesis and examine the effect on discrimination learning tasks. Furthermore, neurogenesis ablation will be combined with mitral cell imaging to test whether mitral cell plasticity during olfactory experience is altered with neurogenesis ablation. I Aim 3, we will evaluate the hypothesis that young adult-born inhibitory neurons show particularly pronounced and adaptive plasticity during olfactory experience. We will do this by directly imaging the activity of age-defined adult-born granule cells throughout the olfactory experience paradigms. These experiments combine cutting-edge technologies including chronic high-resolution two-photon imaging, behavioral tasks by head-fixed mice, and mouse genetics to label or ablate specific neuron types. They will reveal fine-scale circuit plasticity underlying perceptual learning and identify functional significance of adult neurogenesis. RELEVANCE: Dynamic and flexible processing of sensory information is essential for the well-being of animals in a changing environment and often impaired in neural disorders such as schizophrenia. We study neural mechanisms underlying olfactory perceptual learning, with a particular emphasis on adult neurogenesis within the olfactory bulb, the first olfactory center of the brain. The results will not only help us understand the functional significance of adult neurogenesis but also have implications in future treatments of learning disorders such as Alzheimer's disease and aging-related dementia.

? DESCRIPTION (provided by applicant): Sensory inputs are not represented faithfully in the brain. Rather, they are integrated with the information provided by the animal's internal states that are affected by factors including arousal, attention, prediction and experience. This integration process is impaired in psychiatric disorders such as schizophrenia and attention deficit hyperactivity disorder. Despite the scientific and clinical importance, our knowledge is limited as to the neural circuit mechanisms of how external sensory (`bottom-up') information and internally-generated (`top-down') information are integrated and how these processes are affected with experience and learning. We propose to combine cutting-edge techniques to gain a mechanistic insight into the dynamics and regulation of bottom-up and top-down inputs onto the primary visual cortex (V1) of mice. We will study how these processes are influenced by passive sensory experience and learning. Towards this goal, we have developed a visually-guided active avoidance task for head-fixed mice. In Aim 1, we will use chronic two-photon calcium imaging to record the activity of V1 L2/3 excitatory neurons and the sources of their bottom-up and top-down inputs during passive experience and association learning over days. We hypothesize that 1) sensory experience increases the weight of top-down inputs and decreases that of bottom-up inputs and 2) association learning induces L2/3 neurons to signal the potential timing of the associated event and this information is contained in top-down input activity. In Aim 2, we will test how the activity of local inhibitory circuits is influenced by visal experience. Bottom- up inputs to L2/3 neurons arrive at their perisomatic regions while top-down inputs arrive at their distal dendrites. Therefore, dendrite-targeting, somatostatin-expressing inhibitory neurons (SOM-INs) could regulate top-down inputs, and perisomatically-targeting, parvalbumin-positive inhibitory neurons (PV- INs) could control bottom-up inputs. We hypothesize that learning causes a reduction in SOM-IN activity and an increase in PV-IN activity. Such changes could accommodate the shift in the balance of bottom-up and top-down influences on V1 L2/3 neurons. In Aim 3, we will perform manipulation experiments to test some of the predictions of our model. We will test whether the learning-induced shift of activity timing of V1 L2/3 neurons requires 1) top-down inputs from higher cortical areas and 2) the reduction of SOM-IN activity. We will test these possibilities by 1) inactivating higher areas and 2) activating SOM-INs after learning on a trial-by-trial basis using optogenetics while monitoring the activity of V1 L2/3 neurons with calcium imaging. These experiments will reveal fine-scale circuit mechanisms governing dynamic sensory representations and also establish a paradigm to combine cutting-edge technologies that can be applied to other forms of learning and behaviors in the future.

This is a development project to construct a scanning two- and three-photon microscope for deep imaging in the brain in support of activities related to neuronal circuit analysis and neurovascular coupling. The ability to image ever deeper in the brain with optical methods is a key enabling technology in our ability to decipher neuronal anatomy and circuit function as well as neurovascular function. Optical tools, together with labels of specific brain structures, are the only means to probe the geometry and state variables of single cells, e.g., voltage and second messengers, and the dynamics of brain vasculature in a noninvasive or partially invasive manner in vivo. The current method of choice for in vivo imaging makes use of two-photon microscopy with a 100-femtosecond pulsed laser sources to observe structure and dynamics throughout the upper ~ 500 micrometers of cortex of mice. Yet there is a clear need to image throughout the full depth of cortex, 1.0 to 1.2 micrometers in mice, to determine the complete flow of information in cortical processing. There is also a need to image deeper still into hippocampus and other subcortical structures without excavated overlying tissue, as well as to determine the loci of vascular control throughout gray and while matter. The initial proposed experiments, all of which depend on the proposed instrument, address topics in fundamental brain science as well biomedicine. Fundamental issues revolve around neuronal plasticity and memory formation and include: the formation of motor memories, where the learning of a behavioral task is believed to follow from the formation of patterns of correlated neuronal output in motor cortex; the transformation of sensory signals in cortex into memory traces, such as learned fear via the amygdala and induction of depression via the habenula; the role of specific gene products, known as inducible transcription factors, in synaptic plasticity; and understanding how the prodigious adult neurogenesis in the olfactory bulb is integrated into ongoing olfactory function. More applied issues concern the role of exposure to nicotine alone in changing the basis for memory formation, as well as issues in vasodynamics, including the locus for neuronal control of its own nutriment supply through the cortical vasculature and the impact of microinfarctions on cell death within the white matter, where myelinated fibers traffic information from sensory to motor areas that span the cortical mantle. Realization of this system will permit training of graduate students and postdoctoral fellows in state of the art in vivo optical imaging. UC San Diego, along with the greater La Jolla scientific community, supports a large and highly collaborative neuroscience community with graduate students and fellows who will pursue careers at institutes throughout the county, even the world. They will be inspired to think of new experiments based on the capabilities of imaging new vistas in the brain, as well as new associated technologies, particularly in the design of optical probes of yet unmeasured variables. Lastly, the high density of potential users within this community will facilitate unanticipated refinements of deep imaging and perhaps transform the proposed development project into a turn-key design for the benefit of the global neurosciences communities.


The PI proposes to build an instrument, whose design is motivated by three threads of work, that enables two- and three-photon imaging throughout the full depth of cortex and into deeper structures. First is the use of 100-fs pulsed laser light at wavelengths of 1.3 or 1.7 micrometers, where scattering is minimized but absorption by water is still weak; second is the use of an optical amplifier to increase the energy per pulse and drive fluorescence at greater depths, and third is the use of aberration corrective optics to counteract distortion of the incident beam with increasing depth into brain tissue.

? DESCRIPTION (provided by applicant): The anatomical substrates and cellular mechanisms underlying reward-dependent learning have been studied for decades, but the specific circuit and network interactions between the cortex, striatum, and midbrain that mediate action selection have not been systematically investigated. Here, we bring together three different investigators with specialized expertise in each of these three brain regions. As a team, the investigators are well positioned to perform cutting-edge cell-type-, region-, and projection-specific imaging and ontogenetic manipulations of neuronal ensembles during complex behavioral tasks designed to uncover the network-level representation of decision-related variables for action selection. The computational frameworks for analyzing these data are provided by reinforcement learning models. In Aim 1, the focus is on the striatum, which arguably lies at the center of reward-dependent learning. It is the point of intersection for sensorimotor and contextual information from cortex, and reward- and motivation-related information from the midbrain. Using microendoscopy with calcium imaging from genetically-specified neuronal subtypes in different striatal subregions during behavior, we will identify the key decision-related variables represented in the striatum during action selection tasks. In Aim 2, we will image from specific populations of dopamine neurons in the midbrain that project to different striatal subregions, in order to decipher their role in reward-related signaling. In Aim , we will image large ensembles of neurons in the motor cortex (M1 and M2) using two-photon microscopy, with a focus on neurons projecting to distinct cell types and subregions of striatum. All three aims will use the same behavioral tasks, and the same analysis techniques, in order to facilitate the integration of data from all three brain regions into a single coherent model for vertebrate action selection.

Understanding the structural and functional components of the brain that underlie perception, cognition and action, is crucial for developing next generation neural prostheses, brain machine interfaces, and discovering preventive measures against neurological disorders. Optical technologies have enabled us to record and infer neural activity with single-cell resolution. However, they are limited by low temporal resolution, and often fail to accurately capture the neural dynamics at the milli-second time scales. Electrophysiology, on the other hand, provides higher temporal resolution, but single-cell electrophysiology usually suffers from low throughput, and recordings that cover larger spatial scales suffer from poor spatial resolution, making it difficult to decipher neural activity at cellular scale from large areas. Realizing that micro-scale optical imaging and macro-scale electrophysiological recording possess complementary strengths in terms of spatial and temporal resolution, this multidisciplinary project will combine the two recording modalities using innovations in neural engineering, multi-modal imaging and signal processing, to understand neural activity at previously unattained temporal and spatial resolution. Such a capability will lead to new discoveries on information processing in the brain and circuit dysfunctions for neurological disorders (epilepsy, depression, memory disorders, etc.), affecting one billion people worldwide. Recording and resolving neural activity with enhanced resolution can drive the development of next-generation of brain computer interfaces for restoring vision, hearing, and movement. The outcomes of this project will also be integrated into developing interdisciplinary educational materials for training the next generation of neuroengineers, neuroscientists and signal processing experts. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).

The project has three main technical components that consist of development of novel electrode arrays, careful design of multi-modal imaging experiments, and advanced signal processing techniques for solving ill-posed inverse problems. Simultaneous multiphoton imaging and electrophysiology experiments enabled by novel electrode arrays will generate brand new datasets which will be processed by new data-driven super-resolution algorithms that judiciously exploit the complementary strengths of the two imaging modalities. The key idea is to cast the fusion problem within the mathematical framework of bilinear problems, and exploit sparsity of the underlying neural activity as a key ingredient in solving the inverse problem by fusing the datasets obtained from optical and electrophysiological recordings. The mathematical principles and algorithms used for creating super-resolution images by fusing signals with complementary attributes have broader applicability beyond neural imaging, and can be used for developing more efficient solutions for ill-posed inverse problems that arise in diverse imaging applications.

The recent explosion in worldwide data together with the end of Moore's Law and the near-term limits of silicon-based data storage being reached are driving an urgent need for alternative forms of computing and data storage/retrieval platforms. In particular, exabyte-scale datasets are increasingly being generated by the biological sciences and engineering disciplines including genomics, transcriptomics, proteomics, metabolomics, and high-resolution imaging, as well as disparate other scientific fields including climate science, ecology, astronomy, oceanography, sociology, and meteorology, amongst others. In this data revolution, the continuously increasing size of these datasets requires a concomitant increase in available computational power to store, process, and harness them, which is driving a need for revolutionary new, alternative substrates for, and forms of, computing and data storage. Unlike traditional data storage and computing materials such as silicon, the human brain offers a remarkable ability to sense, store, retrieve, and compute information in a manner that is unrivaled by any human-made material. In this research project, analogous modes of information sensing, data storage, retrieval, and computation will be explored in non-traditional computing molecular systems and materials. The over-arching goal of the research is to discover revolutionary new modes of data storage/retrieval, sensing, and computation that rival conventional silicon-based technology, for deployment to benefit society broadly across all domains of data science. Graduate students and postdocs across five institutions will be trained and mentored in a highly interdisciplinary manner to attain this goal and prepare the next-generation of data scientists, chemists, physicists, and engineers to harness the ongoing data revolution. The research will be disseminated to a broad community through news outlets and integration of high school student internships in participating research laboratories.

Large-scale datasets from spatial-temporal calcium imaging of the mouse brain will be recorded into DNA-based, nanoparticle-based, and phononic 2D and 3D soft and hard materials. Continuous spatial-temporal data will first be transformed into discrete data for mapping onto DNA-conjugated fluorophore networks, dynamic barcoded nanoparticle networks, and phononic 2D and 3D materials. Sensing, computation, and data storage/retrieval will be demonstrated as proofs-of-principle in exploiting the chemical properties of molecular networks and materials to recover the encoded neuronal datasets and their sensing and computing processes. Success with any of these three prototypical materials would revolutionize the ability to encode arbitrarily complex, large-scale datasets into complex molecular systems, with the potential to scale across diverse data domains and materials frameworks. The investigators' Autonomous Computing Materials framework will thereby enable the encoding of arbitrary "big data" sets into diverse materials for data storage, sensing, and computing. This project maximizes opportunities for disruptive new computing and data science concepts to emerge from a multi-disciplinary, collaborative team spanning data science, neuroscience, materials science, chemistry, physics, and biological engineering.

This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by HDR and the Division of Chemistry within the NSF Directorate of Mathematical and Physical Sciences.

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.

The brain is arguably the most sophisticated and the most efficient computational machine in the universe. The human brain, for example, comprises about 100 billion neurons that form an interconnected circuit with well over 100 trillion connections. Understanding how a multitude of brain functions emerge from the underlying neuronal circuit will give insights into the operating principles of the brain. In this award, a multidisciplinary team of systems biologist, computational biologist, material scientist, neuroscientist, and machine learning expert will work synergistically to leverage the data revolution in neuroscience to answer a fundamental question: How does the brain learn, store, and process information? The team will develop and apply advanced data analysis algorithms to harness the great volume of neuronal data generated by the latest imaging and molecular profiling technologies, for elucidating the neuronal circuits driving brain functions. Computer simulations of a spin-electronic (spintronic) device will further serve as a platform to validate and emulate important operational characteristics of such neuronal circuits. The award sets the groundwork for an interdisciplinary data science research and educational program that will bring a new and powerful paradigm for studying brain functions as well as for designing transformative brain-inspired devices for information processing, data storage, computing, and decision making.

The project has a specific focus on an essential function of the brain: motor-skill learning. This function emerges from the underlying circuitry of neurons that governs the activities of molecular signal transmission and neuronal firing. Importantly, the neuronal circuit in a mammalian brain is highly plastic and dynamic, features that endow animals with the ability to respond to myriad external stimulations through learning. By harnessing the latest data revolution in neuronal imaging, single neuron molecular profiling, spintronic device simulation, network inference, and machine learning, a team of multidisciplinary investigators will be supported by this award to investigate the fundamental principle of neuronal circuit rewiring that drives brain?s learning function. More specifically, the team sets out to achieve the following specific tasks: (A) Infer learning-induced rewiring of large-scale neuronal networks from two-photon calcium imaging data through the development of novel and powerful network inference algorithms; (B) Build biochemical-based models of neuronal circuits by integrating molecular profiling with neuron firing and connectome dynamics; and (C) Develop a spintronic material network model that emulates learning and memory formation by exploiting the spin dynamics in spintronic materials. The project seeks to lay the foundation for the creation of an interdisciplinary data-intensive brain-to-materials initiative that will be applied to understand and emulate the operational principles of brain neuronal circuits underlying learning, cognition, memory formation, and other behaviors. The outcomes of the initiative will have a paramount impact on the society, not only in our understanding of the brain and its functions, but also in overcoming current bottlenecks of existing computing architectures. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.

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.

Project Summary The regulation of synaptic changes during learning involves intricate interactions among distinct cell types. Neuromodulators such as dopamine (DA), acetylcholine (ACh), and norepinephrine (NE) are known to modulate learning-related plasticity. However, the precise cellular loci of these neuromodulators at which they regulate synaptic changes during learning are not fully understood. This is largely due to the fact that in vivo studies of neuromodulator contributions to learning often employ lesion, pharmacology and/or whole-animal knockouts, which do not restrict the manipulations to specific cell types. To address this issue, we propose to establish a novel CRISPR-based method to remove targeted genes of interest in a small subset of cortical neurons. The gene editing will be Cre-dependent and therefore can be applied selectively to specific cell types using Cre lines. The modular nature of the method will make it straightforward to target multiple genes in single neurons, enabling double, triple, and quadruple knockouts. The method also allows specific labelling of these sparse knockout neurons with GFP, which allows us to perform longitudinal imaging of synaptic structures of the genetically modified cells in vivo during learning. Using this CRISPR-based sparse knockout and fluorescence labelling method, we will investigate the requirement of various neuromodulator receptor genes in cortical plasticity during learning. This investigation will take advantage of a system in which we have made pioneering observations of cell-type specific synaptic changes during motor learning (Peters et al., Nature 2014; Chen et al., Nature Neuroscience 2015). Specifically, our lab has identified three distinct, cell-type specific synaptic changes in the motor cortex during motor learning, in principal excitatory neurons, somatostatin-expressing inhibitory neurons (SOM-INs), and parvalbumin-expressing inhibitory neurons (PV-INs). These cell-type specific but interrelated plasticity events likely involve parallel signaling pathways, and each of these plasticity events might require a distinct set of neuromodulator receptors in a cell-type specific manner. Therefore, this motor learning paradigm provides an ideal platform to disentangle the diverse potential contributions of neuromodulator signaling in each cell-type and the CRIPR-based sparse knockouts will be pivotal for us to systematically probe cell-autonomous gene functions in vivo. In addition to the proposed application, the established method will be widely applicable to other genes and biological systems in the future.

Olfactory information is first processed by the neural circuits in the olfactory bulb. It is now widely appreciated that the olfactory bulb circuit is modified in an experience-dependent manner. An especially dramatic example of plasticity in olfactory bulb circuits is adult neurogenesis, in which thousands of newly born neurons are incorporated into the bulbar circuitry as local inhibitory neurons every day throughout adulthood. The majority of these adult-born neurons (ABNs) become granule cells that provide inhibition through the spines at their apical dendrites onto the principal mitral/tufted cells. In this proposal, we will characterize the synaptic structural plasticity of ABNs in the olfactory bulb and investigate its context-specificity and mechanisms. Understanding the detailed mechanisms of context-specific plasticity would have an important impact on clinical disorders such as Alzheimer's disease, age-related dementia, and post-traumatic stress disorders. Our central hypotheses are that 1) ABNs increase the density of their apical dendritic spines during learning of an olfactory discrimination task but not during passive experience of the same odorants, and 2) this context-specificity of ABN plasticity is ensured by feedback projections from the piriform cortex to the olfactory bulb which increases dendritic activity of ABNs during task learning. Such a context-specific recruitment of ABN inhibition could provide the basis for stimulus-specific inhibition to promote the pattern separation of representations of task-relevant odorants. We will address these hypotheses by combining in vivo two-photon structural imaging, in vivo two- photon calcium imaging, behavioral task in head-fixed mice, and pathway-specific optogenetics. We have been pioneering the use of these techniques in studying the dynamics of olfactory bulb circuits (Kato et al. Neuron 2012, Kato et al. Neuron 2013, Boyd et al. Cell Reports 2015, Chu et al. Neuron 2016, Chu et al. eNeuro 2017). In particular, we will leverage on our recent study that showed that ABNs are uniquely required for the learning of fine olfactory discrimination (Li et al. eLife 2018). In Aim 1, we will investigate the age- and context- specificity of granule cell synaptic plasticity in vivo and test the hypothesis that young ABNs uniquely increase their spine density during learning. In Aim 2, we will examine the dendritic calcium activity of ABNs as a potential cellular mechanism regulating ABN dendritic plasticity. In Aim 3, we will address the role of feedback projections from the piriform cortex to the olfactory bulb as a potential circuit mechanism that ensures the context specificity of ABN plasticity. These aims represent a systematic approach to investigate the mechanisms of how behavioral context can affect the plasticity of an olfactory circuit.

Learning involves the reorganization of the complex connectivity schemes between neurons in the brain. Critical to this re-wiring is the formation of new synaptic connections, and thus the study of the rules governing synapse formation remains a central focus of neuroscience. Over the past decade, it has become increasingly clear that synapses follow specific spatial organizational principles, such that functionally similar synapses tend to ?cluster? on dendrites. This spatial patterning likely affords computational advantages that allow neurons to efficiently construct representations of their input repertoire. However, it is unknown whether and how new synapses that form during learning contribute to such functional clustering of synapses. We propose to use a combination of cutting-edge imaging techniques to investigate new spine formation - and their potential clustering patterns - over learning, integrating a detailed description of the local synaptic activity profiles with a deep interrogation of the cellular and subcellular anatomy of the surrounding tissue. We will do this by first applying longitudinal functional imaging of dendritic spines in vivo in mice learning a motor skill over 2 weeks, followed by 3D electron microscopy of the volume imaged in vivo for a high-resolution reconstruction of relevant structures, including those that are not labeled for in vivo imaging. This approach will reveal how both the structural and functional environment of neuronal dendrites relates to the formation of new synapses. By focusing on new synapses whose activity becomes coherent with other nearby synapses on the same dendrite, we will provide a thorough description of how spinogenesis during learning contributes to functional synaptic clustering. Furthermore, by reconstructing the nearby cellular structures, we will provide heretofore inaccessible details, such as whether such functionally related synapses share the same axonal inputs. Finally, by using a well characterized model of learning, this work will also allow a quantitative description of how new spines and the clusters that they form relate to specific features of a learned behavior. The experimental paradigm established in the proposed project will be widely applicable to the studies of neural circuits underlying other types of learning and behavior. The prevalence of neurological diseases affecting neuronal connectivity highlights the importance this pursuit.

PROJECT SUMMARY Our decisions are influenced by recent experiences (i.e., history dependence). The control of history dependence relies on the posterior parietal cortex (PPC). However, PPC also serves many other functions such as working memory and sensorimotor control. This proposal aims to elucidate how PPC circuits perform multiple functions, a critical step towards identifying the causes and treatments of specific PPC malfunctions. We hypothesize that different functions involve different subcircuits in PPC, each with distinct input-output connectivity. We will test this hypothesis, focusing on the function of history dependence and two projection targets of PPC, the dorsal striatum (STR) and posterior secondary motor cortex (pM2). Although both targets have been implicated for history-dependent decisions, our preliminary results indicate that the PPC neurons projecting to each target are largely distinct, suggesting that the two pathways may have distinct functions. Here we propose to take three parallel approaches to uncover which of the two pathways mediates history dependence and to characterize long-range inputs to each pathway. First, we will compare the history bias information encoded by PPC neurons projecting to STR or pM2, using a combination of two-photon calcium imaging and retrograde labeling in mice exhibiting history-dependent decision bias. Second, we will determine the necessity of PPC-STR and PPC-pM2 pathways for history-dependent decisions, by optogenetically inactivating each pathway and assessing its effect on history-dependent bias. Lastly, we will map long-range inputs specific to the two pathways that contribute to the formation of history-dependent bias in PPC, using retrograde trans-synaptic rabies tracing. Together, the results will reveal whether PPC contains a subcircuit dedicated to history dependence and what constitutes the input and output pathways of that subcircuit. Answers to these questions will lay the solid groundwork for future research that delineates the inter-areal circuits underlying decision-making and other PPC functions. Furthermore, the identification of function-specific pathways may point to neural substrates for targeted intervention with less risk of compromising other functions.

Learning and memory are cognitive functions that are central to human behavior. It has been widely hypothesized that multiple brain regions are coordinated with hippocampus, a subcortical structure, to form the basis for learning and long-term memory. Understanding how different brain regions interact during learning can lead to better understanding of long-term memory storage in the brain. This high-risk, high-payoff project will investigate how cortex and hippocampus communicate and coordinate information transfer during learning and memory consolidation by multimodal imaging and recording experiments. However, such experiments are not currently feasible due to technical limitations. This proposal follows a transformative approach to investigate hippocampus-cortex coordination during learning and memory by combining (i) technological breakthroughs in development of novel implantable probes, (ii) carefully designed multi-modal sensing experiments, and (iii) advanced data analysis techniques. Such a capability could lead to discoveries on information processing in the brain and can help to better understand circuit dysfunctions causing memory impairment for various neurological disorders affecting a large population worldwide. Findings from this research can help with bridging critical gaps between artificial intelligence-driven models for learning and real biological learning in brain. Understanding the latter has the potential to reshape current practices in machine learning. This project will also provide opportunities for students to become engaged in cutting-edge multidisciplinary research in microfabrication, neuroscience and data analysis. The project will also provide research internship opportunities and mentoring initiatives for underrepresented minorities in engineering.

The objective of this project is to investigate how cortex and hippocampus communicate and coordinate information transfer during learning and memory consolidation by multimodal imaging and recording experiments. Wide-field calcium imaging will be used to monitor cortex-wide neural activation across large areas in awake mice. Simultaneous electrophysiological recordings from hippocampus will detect high frequency oscillations such as sharp-wave ripples and spikes from single neurons. Integration of multiple imaging and recording modalities requires development of new implantable probe technologies enabling recording from hippocampus during imaging and advanced data analysis techniques. Complementary expertise of the investigators will be leveraged to pursue; Task 1: Development of new flexible penetrating microprobes compatible with optical imaging, Task 2: Multi-modal, multi-scale experiments in awake mice generating brand new data sets synergistically combining information from calcium fluorescence, local field potentials, single units and behavior, and Task 3: Development of a novel data-driven task-aware algorithm to perform single-event analyses with multimodal calcium imaging from cortex and electrophysiological recordings from hippocampus.

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.