Daniel A. Dombeck - US grants

DESCRIPTION (provided by applicant): The dendritic trees of neurons in the brain's cortex conduct elemental brain functions such as information processing and learning. Research in brain slices has provided a wealth of information about the dendritic mechanisms that could be operating in behaving animals to integrate, and alter the strength of, synaptic inputs from presynaptic sources. Key among these mechanisms are back-propagating action potentials and nonlinear integration of synaptic inputs leading to dendritic spiking, which confer to the dendriti tree a host of local and global signaling possibilities. Numerous alluring models of information processing and learning have arisen as a result of these in vitro findings, but currently almost nothing is known about which of these mechanisms are at work in awake, behaving animals. The present proposal leverages recent technical advances developed by the PI that enable functional imaging of calcium transients with sub-cellular resolution in the hippocampus of head-restrained mice performing spatial behaviors in a virtual-reality interface. Using these methods, the research proposed in this grant application will allow us to bridge two disconnected areas of neuroscience research: studies that characterize the firing patterns of hippocampal neurons in behaving rodents, and experiments that study the mechanisms underlying firing and plasticity in these cells in reduced preparations. Specifically, we aim to determine the behavioral relevance of the fundamental dendritic mechanisms of bAPs and dSpikes in the hippocampus. This will allow testing of models of plasticity which have been developed based on in vitro data, across a wide range of parameter space, to finally establish which learning mechanisms are behaviorally relevant.

A fundamental question in neuroscience is to understand how different sets of neurons in a network combine or integrate their various inputs, and how both the pattern of inputs and the resulting outputs are related to behavior. Novel, even unprecedented, experimental methods have been and are being developed with which to image or record from large numbers of distributed neurons but understanding the integration step can be difficult since inputs and outputs are generally distributed over many millimeters, and it is very difficult to record from both simultaneously, especially so in a behaving animal. This project will combine experimental and computational methods to elucidate such synaptic integration in pyramidal neurons associated with mammalian spatial navigation in awake, behaving animals. The imaging data acquired from awake behaving mice will be made available to other groups and the full results will serve as a model for other research concerned with the integration of inputs in networks of neurons. The computational models will be made available on the ModelDB database and will be a resource to others working to understand other aspects of functionality in this brain region. Furthermore, the work will involve a close collaboration between experimental and computational research groups, thus giving postdoctoral fellows and graduate students cross-disciplinary research training.

In pyramidal neurons of the hippocampus, the large dendritic tree constitutes an elaborate network of branching processes involving tens of thousands of excitatory synapses containing a variety of voltage-gated ion channels. The pattern of synaptic inputs impinging upon the dendritic arbor and the degree to which these inputs are processed by it to drive place field firing (i.e., firing correlated with spatial location) during behavior are currently unknown. The goals of the project are first to 1) develop improved computational models of dendritic place cell firing constrained by current imaging data and 2) establish new experimental techniques to image the inputs to pyramidal cells in the dendritic tree, at single spine resolution, during place field firing. Together the experiments and models will be used to 3) determine the degree to which local dendritic processing is involved in place cell firing. The proposed experiments will allow for the construction of significantly improved models of hippocampal function and the models will provide a framework within which to understand activity recorded at a local level in the dendritic tree and assemble a comprehensive picture of dendritic processing across the whole arbor.

The neurotransmitter dopamine (DA), produced by midbrain DA neurons, influences a spectrum of behaviors including motor, reward, motivation, and cognition. In accordance with these functions, DA dysfunction is prominently implicated in a wide gamut of disorders affecting tens of millions of people, including Parkinson's disease, schizophrenia, ADHD, addiction and depression. Understanding how DA neurons control all of these distinct behaviors is important for understanding and treating these neuropsychiatric diseases. The literature is dominated by anatomical classification of DA neurons based on location within the Ventral Tegmental Area (VTA) or Substantia Nigra pars compacta (SNc). Guided by an emerging literature on DA neuron heterogeneity, we hypothesize that there must exist several molecularly and functionally distinct DA types, perhaps intermingled, that could underpin the myriad functions of DA. As a first step in classifying DA neurons, the Awatramani lab developed an approach to profile single midbrain DA neurons, each for the expression of 96 key genes, using a microfluidic dynamic RT-qPCR array. Hierarchical clustering indicated that DA neurons exhibited roughly six distinct molecular barcodes, presumably indicative of at least six molecularly and functionally distinct DA subtypes. The Dombeck laboratory has developed a robust data set demonstrating functional heterogeneity of DA neurons in behaving mice. Previous models postulated that slow variations in tonic firing rates bias the system toward or away from movement, whereas phasic signaling was linked to unpredicted rewards. Using imaging in behaving mice, we showed heterogeneous expression of phasic locomotion and reward signaling in DA axons projecting to the striatum. In the dorsal striatum we found that most DA fibers displayed a phasic signal locked to the animal's cyclic accelerations during locomotion. In the ventral striatum, axonal signaling to unpredicted rewards was more prevalent. These results indicate that striatum DA release is not simply homogenous and movement permissive, but is richly heterogeneous with respect to reward and locomotion signaling. Based on these complementary data sets- molecular heterogeneity and functional heterogeneity, our goal is to correlate molecular identity with anatomy and function. In Specific Aim 1, we will define the diversity, transcriptomes, and projections of DA neuron subtypes, developing intersectional genetic tools to access DA neuron subtypes. In Specific Aim 2, we will establish the behavioral signaling properties of the genetically identified DA subtypes that project to the striatum. Thus, using a collaborative approach between two laboratories each with distinct expertise, we aim to characterize DA neuron subtypes based on their molecular, anatomic and functional properties. These studies will be vital for designing targeted therapies for the DA system. Moreover these studies will provide genetic platforms for manipulations of DA subtypes towards understanding their role in mammalian behavior.

We think of nervous systems as the means by which an animal organizes its world, but a deep time perspective suggests that it is rather the world of an animal that organizes its brain. Prior to the vertebrate invasion of land 385 million years ago, vision, our most powerful long-range sense, took in a largely blurry world at short range while underwater, with little variability in scene as the eyes move. Once on land, vision takes in a high contrast world at long range, with high variability as the eyes move. A possible reason for the greatly increased size and complexity of terrestrial vertebrate brains over those of fish is that this environment provides selective advantage to long sequences of actions toward distant goals, reaching its most complex form in varieties of prospective cognition in certain mammals and birds. A team of Northwestern researchers will conduct research into the computational, behavioral, and neural basis of planning, rooted in an evolutionary and computational sensory ecology perspective and a commitment to ethologically relevant behaviors. Planning is an immensely important capacity to understand the mechanistic basis of, as it participates in a diverse range of behaviors, and its diminishment favors impulsivity and reliance on the habit system. Up to now, laboratory studies of planning have typically relied on reduced environments and simple behaviors which are either appetitive or (more rarely) aversive, without a sentient target, the dynamics and unpredictability of which is likely key to the adequate analysis of prospective cognition. Methods from neuroengineering and data-intensive neuroscience will be brought to bear on the problem of making a more ethologically relevant, yet tightly controlled approach to investigating planning possible. The computational and behavioral work will be used to guide neurobiological interventions in two of the key brain structures that participate in reactive versus reflective decision making and choice: the striatum and hippocampus.

The team will pursue research with an unusually bold intellectual dynamic range well beyond a typical disciplinary approach, from its motivation rooted in evolutionary biology and computational sensory ecology, to the extension of the latest machine learning methods, through to single-cell resolution imaging of live animal behavior in a virtual reality system. The researchers will knit together parallel synergistic efforts in the simulation of planning, a mechatronically reconfigurable behavior arena with a robot predator, and two-photon single cell resolution imaging in a virtual reality system, resulting in an ethologically relevant context significantly more complex than current practice in laboratory settings. There are few areas of neuroscience that have as much potential to impact society as research on the neural basis of planning. Discussions of self-control, marshmallow tests, grit, and challenges we face in making long term plans such as retirement or adapting to changing climate for future generations fill the media. One of the team's research goals is to understand the manner in which the nervous system participates in constraining the temporal and spatial range of prospective cognition,which is clearly quite limited even in humans, toward a neuroscience of sustainability.

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.

Planning is centrally important for everyday life. Planning is challenging to study, as it involves an internal search through future possibilities for action, in the absence of any sign this is occurring from the outside. The project will investigate two synergistic aspects of real-time planning: its biology and technology. Current artificial intelligence methods require vast amounts of power, a large amount of training, and examination of millions of possible futures to tackle simple problems such as the next move in a turn-based board game. In contrast, mammals require very little power, little to no training, and examination of few possible futures to tackle complex problems such as where to go to hide from a stalking predator. This project will develop a new online planning agent—a robotic “predator”—that will interact with animals trained to evade it inside a complex habitat. The robot will interact with laboratory animals whose brain activity is being recorded while they are challenged—via specially-designed complex habitats—to employ strategic behaviors in avoiding the robot. This will test and advance theory of neural mechanisms underlying the everyday ability to plan in real time in an energy-efficient manner.

The ability to plan actions can produce much larger rewards than reactive, reflexive, or habitual behaviors. Whereas humans exhibit great proficiency in planning and executing daily movements, poor response to long-term threats shows its limits. Research on multi-step planning is in its infancy, constrained in part by behavioral tasks with low ecological validity. Theory has advanced due to rapid progress in artificial intelligence, but most formalizations require so much computing power that real-time planning is impossible. Animals seem likely to form real-time plans in some other way. In prior work, the PIs showed that a selective benefit of visually guided planning may have facilitated the transition onto land 380 million years ago because animals can see targets much farther in air than through water. The benefit of planning in predator-prey engagements is maximized in habitats that afford long sightlines while also providing obstacles that can hide adversaries. In these conditions, such as savanna-like habitats where hominins first emerged, planning its peak advantage. In Aim 1 of the project, this idea is modeled to identify locations of maximal planning payoff (via a network connectivity measure) and used to predict neural computation in animals. This initial algorithm is 10,000 times faster in achieving the same survival rate of simulated prey than a leading competitor in machine learning. This enables creation of a behavioral assay in which live animals are challenged by an adversary with similar planning abilities to their own. With the principle translated into hardware, a bidirectional benefit will emerge for Aim 2. First, neural activity—using Neuropixels probes in freely behaving mice—will be compared to the team’s theory predictions in real-time; they predict that boundary detection cells in the hippocampus and delay interval cells in entorhinal cortex are important for trimming the neurocomputational burden of plans. Second, during recordings, animals will engage with a robot that plans in real time.

This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NCS), a multidisciplinary program jointly supported by the Directorates for Biology (BIO), Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).

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 This application seeks to renew the Neurobiology of Information Storage Training Program (NISTP), which is devoted to preparing exceptional predoctoral students for research-intensive careers in the science of learning and memory. Active since 2003, the program is focused on training in fundamental mechanisms of information storage in animals and humans, acting as a central organizer for research in this area at Northwestern. With the recent shift in mental health research toward dimensional, systems-based frameworks there is unprecedented need for training of the kind offered through this program, which provides training at multiple levels of analyses for understanding mechanisms of learning and memory and their relevance to mental health. The NISTP is based in the Northwestern University Interdepartmental Neuroscience (NUIN) program, emerging from a multidisciplinary group of 29 interactive investigators who have successfully engaged in collaborative research on molecular/genetic, cellular/circuit, and systems/behavioral determinants of information storage. NISTP preceptors have strong track records in predoctoral training and well-funded research programs, and can impart both basic and clinical perspectives to a group of outstanding developing scientists. Training components of the NISTP will include: 1) an advanced course in the latest research in information storage neurobiology, taught by NISTP preceptors; 2) two trainee-hosted lecture series featuring leading investigators in the field of information storage; 3) mock study sections for trainees preparing NRSA applications; and 4) quarterly NISTP meetings including a) ?research in progress? trainee talks, b) ?bench to bedside? translational discussions, c) ?computational modeling and memory? interactive discussions, and d) the annual NISTP retreat. NISTP also serves to educate students in the ethics of science and to recruit students from underrepresented groups to study learning and memory. Continuous evaluation of the program will be accomplished using qualitative mechanisms, such as evaluations by trainees, and quantitative measures, such as tracking the research productivity, funding, and career trajectories of former trainees. An internal Steering Committee and an External Advisory Committee will conduct additional assessment processes.Trainees are drawn primarily from a pool of NUIN students who have completed most of the required coursework and have We r equest continued support for five trainee slots, which will be supplemented with one institutionally funded ?affiliate? slot . With the value added by the NISTP, we are confident that NISTP trainees will emerge from their graduate training poised to advance research in fundamental biological mechanisms of learning and memory and well positioned to develop novel translational applications. made significant progress in their thesis research.