Grants

NIGMS - National Institute of General Medical Sciences

Project summary Directional cell migration is crucial for many physiological processes, including angiogenesis, wound healing, tissue remodeling, and immune responses. Dysregulated cell migration is associated with various diseases, such as fibrosis and metastatic cancer. Understanding the molecular mechanisms underlying cell migration has broad implications for human health. Cells employ different modes of migration—collective, mesenchymal, amoeboid, etc.—depending on their intracellular state and extracellular environment. Regardless of the mode, cell migration fundamentally requires coordination of cytoskeletal components (e.g., actin, microtubules, intermediate filaments), extracellular environment sensing, and the generation of physical forces for locomotion. Previous studies have highlighted the critical role of microtubules in various aspects of cell migration. While microtubules undergo several post-translational modifications, how these modifications influence cell migration remains largely unexplored. Microtubule acetylation (acetylation of Lysine-40 on α- tubulin) and detyrosination (removal of the carboxy-terminal tyrosine residue on α-tubulin) have been implicated in metastatic cancer, underscoring the need for a mechanistic understanding of their effects on cell migration and cytoskeletal dynamics. However, elucidating these molecular pathways is technically challenging due to the lack of tools to acutely and specifically manipulate these modifications in living cells. To address this gap, I have developed genetically encoded, light-inducible molecular actuators—optoTAT and optoVASH—to specifically and reversibly induce microtubule acetylation and detyrosination, respectively, in living cells. Over the next five years, we will employ these optogenetic tools alongside genetic and pharmacological perturbations to investigate several critical, yet currently intractable, questions: (1) What are the effects of microtubule acetylation and detyrosination on actin and vimentin dynamics, and what molecular signaling pathways mediate these cytoskeletal interactions? (2) How do microtubule acetylation and detyrosination influence cell contractility, mechanosensing, and engagement with the extracellular matrix? (3) What are the effects of these modifications on the mode of migration in two-dimensional environments (flat surfaces) and three-dimensional matrices? Results from this study will illuminate an underexplored area of cytoskeletal biology—microtubule post-translational modifications—and identify key factors that could serve as diagnostic markers or therapeutic targets.

Regional Council Capital Fund - RC3

Craft Food & Beverage Center Capital

Craig Hospital Foundation

Craig Hospital Foundation is an active grantmaking foundation based in Englewood, CO. Focus: health. Contact the foundation directly for current funding opportunities.

Norwalk Town Treasurer

Priority

Harney County

Crane Community Water and Sewer Study

Port of Port Orford

Crane Replacement

NSF

Understanding how information is processed and propagated in neural circuits in the brain, requires the connections among extremely large numbers of densely packed, intermingled neurons to be accurately mapped out. Despite the century-long effort to map brain circuits, dissection of these complex networks remains technically challenging and time-consuming. This project builds on recent advances in genetic, imaging, and computational methods to develop a unified framework for reliable reconstruction of genetically identified neurons and their connections at single-neuron resolution from 3D image data. The resulting computational methods will be applicable to a wide range of problems in biological and biomedical image analysis. The technical aims of the project are divided into four thrusts. The first thrust generates a large number of super-resolution three dimensional (3D) images of whole Drosophila brains, in each of which connected neurons are labeled by multispectral trans-Tango/Bitbow labeling. The second thrust creates computer vision and unsupervised machine learning algorithms to generate neuronal tracing, and software to aid efficient human proofreading and error correction. This will allow the generation of gold-standard neuronal tracing and segmentation from relatively sparse Brainbow labeling as training inputs. The third thrust creates a generative machine learning model to create realistic Bitbow neurons and synthesize images with various labeling densities based on the sparse annotation training inputs. The fourth thrust develops an annotation machine learning model to reconstruct the densely labeled trans-Tango/Bitbow Drosophila brains with the machine-learning-generated Bitbow images and their corresponding ground truth annotations as training inputs. Together, these efforts form a novel computational framework to enable accurate automatic reconstruction of densely labeled neural circuits. 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.

NSF

As promising as recent artificial intelligence (AI) advances have been, there are still many areas where it falls short. In those areas where it does well, training is slow and requires an unsustainable amount of energy. This has been measured using video games. Humans are able to play these games well after only a few tries, while AI systems require hundreds of thousands to millions of exposures to achieve the same performance. Here, a team of Northwestern University researchers investigate how an animal's ability to complement experience with the use of something similar to imagination underlies the biological learning advantage. In one phase of the work, first-of-its-kind experiments are done with animals evading an autonomous robot threat while large numbers of brain cells are being monitored to decode how the brain is using imagination during avoidance of the robot. In another phase, state of the art methods in machine learning are used to compare to animal performance, while simultaneously building new AI technology to capture the high efficiency learning the researchers observe in biology. With the anticipated need for electricity to fuel AI significantly outpacing infrastructure to provide it, breakthroughs in closing the gap between the energy efficiency of animal intelligence and inefficiency of machine intelligence will be critical to maintain US technological leadership in the coming decades. Leading reinforcement learning algorithms tackle learning using either an explicit model of world (model-based) or use model free methods. Model-based methods appear to perform better in nonstationary environments with partial observability, which is the hallmark of naturalistic scenarios that animal brains have evolved to rapidly master for the sake of survival. Both approaches, however, suffer significant sample efficiency issues. A perspective is emerging that model-based methods may be best for the learning phase to help with sample efficiency, based on results with muZero and with Dreamer v3. At the same time, non-local activity of the hippocampus may be a nexus of memory, planning, and learning, in a manner that suggests a convergent result across artificial and biological systems, where Dyna-style virtual learning substitutes for trial and error in the real world. This perspective, however, leaves open how decision-time planning, in contrast to background planning, finds its niche. The present investigators test the hypothesis that the niche is characterized by high stake contests where partial observability and nonstationarity combine to prevent background planning from being an adaptive strategy. This project is supported jointly by the Neural Systems Cluster in the Directorate for Biological Sciences and the Cognitive Neuroscience Program in the Division of Behavioral and Cognitive Sciences of the Directorate for Social, Behavioral and Economic 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.

NSF

A defining characteristic of human motor behavior is the ability to effectively control movements during physical interactions with the environment. This remarkable capacity depends on the nervous system’s ability to integrate multiple control policies, including force control, impedance control, and feedback control. However, the underlying neuromuscular mechanisms that facilitate such robust and skillful movement control in new environments are still not well understood. This Collaborative Research in Computational Neuroscience (CRCNS) project seeks to elucidate how the human brain and body coordinate movement strategies that combine force, impedance, and feedback control during physical interactions. Gaining insight into these processes will yield significant benefits by advancing foundational neuroscience, enhancing the design of wearable robotics and assistive technologies, and informing innovative approaches to neurorehabilitation for individuals with motor impairments. Furthermore, the project promotes international collaboration between research groups in the United States and France while providing hands-on training opportunities for graduate students and postdoctoral researchers in experimental neuroscience, robotics, and computational modeling. By deepening knowledge at the intersection of neuroscience and engineering, this work aligns with national priorities around automation, health, scientific innovation, and education. This research develops and tests a novel theory of neuromuscular control that predicts how humans coordinate force, impedance, and feedback responses during physically interactive tasks. The approach integrates three key elements: (1) an optimal control framework capable of modeling coordinated force and stiffness control; (2) the incorporation of a computationally efficient muscle model that captures the mechanical properties of muscle force and impedance within the optimal control framework; and (3) the experimental validation of model predictions through experiments that merge robotics with functional MRI to investigate the neuromuscular control involved in tasks requiring physical interaction. Model predictions will inform the design of behavioral experiments where participants are subjected to controlled dynamic perturbations while performing movements primarily involving the wrist joint, both inside and outside the scanner. These experiments aim to test hypotheses regarding the differential expression of specific neuromuscular strategies and identify their neural correlates. By linking computational predictions to both muscular and brain activity, this project will elucidate how the nervous system flexibly deploys distinct control policies in response to varying task demands. 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.

NSF

To behave adaptively, people need to flexibly align their decisions with their immediate and long-term goals. Failures of such goal-directed decision-making can result in an array of maladaptive behaviors, such as financial risk taking, drug abuse, or gambling. While past research has helped identify the drivers of goal-directed decisions, there are still significant gaps in our understanding of how people are able to reorient those decisions as flexibly as they do, as well as when and why they fail to do so. In a series of studies that combine computational modeling with measures of behavior, attentional focus, and brain activity, we are studying the capacities and limits of human decision-makers to flexibly adjust their information search and actions to different goals and task demands. By uncovering the hidden levers and potential failure modes of goal-directed behavior, our work better disentangles sources of real-world decision-making failures, and provide a path towards targeted interventions to better align choices with long-term goals. Our project addresses critical gaps in research on the neural and computational mechanisms that link decision-making and cognitive control. Past work on the computational and neural mechanisms of goal-directed decision-making has been singularly focused on a narrow subset of human goals (how people select the best option from a set). It is therefore unknown how people flexibly reconfigure to their wider array of goals – for instance, selecting under different criteria or accumulating information across options rather than comparing between them – and when and why they fail to do so. Our project develops and tests a computational framework that accommodates the range of flexibility observed in human decision-making, by representing explicit choice goals that define a) how information is translated into evidence and b) how evidence is then integrated to select responses. We are testing our framework in a series of studies that combine behavioral tasks with eye-tracking, EEG and fMRI. This multi-modal approach allows us to uncover the neural circuits and dynamics that enable people to flexibly transform and integrate information about their options to achieve their current goals, and to understand how and why people vary in these capacities. The project further supports outreach activities aimed at training researchers in computational methods for predicting and testing a wide array of decision-making behavior. A companion project is being funded by the Federal Ministry of Education and Research, Germany (BMBF). 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.

NSF

Large neuroscience datasets have been produced that characterize the properties of individual neurons and their connections, and neural activity during behavior. Even with this wealth of data, mechanistic understanding of how function arises from the brain is still extremely limited. How do different brain areas work together to represent the world that is perceived? How are communications among different areas -- which depend on stimuli in the environment and the state of the observer -- implemented by a circuit which is fixed? This project will integrate vast datasets into a set of models which will be probed to address mechanistic questions involving multi-area interactions such as these. More specifically, a set of models which are similar in nature will be constructed. This set will start simple, but gradually simplifying assumptions about model parameters will be replaced by optimizations based on dynamical and functional data. The models will be used to test the hypotheses that: 1. The cell-type specific patterns of connectivity between areas are important in determining temporal responses to simple stimuli and observed frequency-specific inter-area interactions. 2. The functional specificity of inter-areal connections is important in determining the responses and inter-area communication in response to complex stimuli. 3. Variations in single-cell properties explain arousal-related changes in visual responses and oscillations. This project combines the unique experimental datasets and expertise of the Allen Institute with the Jülich expertise and resources for large-scale simulation, and the expertise of both partners in data-based neural network modeling. To facilitate further scientific inquiry, the project will construct a set of detailed system-wide models to study how properties of inter-area connectivity influence inter-area interactions, communication, and stimulus representations. Beyond the specific results for the visual system, addressing the mechanisms of inter-area communication is likely important for a mechanistic understanding of cognition. A companion project is being funded by the Federal Ministry of Education and Research, Germany (BMBF). 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.

NSF

What you see, think, and do is determined by the electrical activity of cells in your brain. These cells are called neurons. Some neurons have electrical activity that is externally determined by the environment, such as neurons in the eye that sense light. However, most neuronal activity is internally generated by neuron-to-neuron signals sent and received at anatomical communication sites called synapses. Understanding how the brain works therefore amounts to discovering how neuronal activity is generated by the brain’s immense number of synaptic connections. For decades neuroscientists have tried to do this using mathematical models called neural networks, but a lack of experimental data has left it unclear whether these models are accurate enough. This Multilateral Research Project combines mathematical modeling, neuronal activity measurements, and synapse-resolution neuroanatomy to build and test biologically realistic neural network models of visual functions. The project capitalizes on recent experimental breakthroughs to build testable models and benchmark a general theoretical framework for modeling the brain and making experimental predictions. It will produce advanced theoretical methods, better annotated datasets, and new experiments that will be freely shared with the scientific community and published in publicly accessible forms. The project heralds a new era of neural networks research, and the research team will help other scientists enter this exciting new field by organizing workshops at mainstream conferences. The work spans three continents (USA, Germany, Japan) and provides rich educational opportunities for undergraduates, graduate students, and postdoctoral fellows to learn the value of multidisciplinary collaboration and international partnership. Quantitatively linking synaptic connectivity and neuronal activity is fundamental towards understanding the brain as a neural network. The three Principal Investigators all provide complementary and indispensable expertise towards this goal. The theoretical foundation for the proposal is a mathematical framework developed by the US Lab for comprehensively analyzing all synaptic weight matrices that allow a neural network model class to perform a specified computation, an approach called ensemble modeling. Ensemble modeling allows one to pinpoint strong predictions that hold consistently across the ensemble, and testing these predictions can validate or rule out an entire model class. These tests are possible in the zebrafish optic pretectum because the German Lab has recently acquired and published a Function-Linked (FuL) connectomics dataset that combines cellular-resolution functional imaging and synapse-resolution connectomics in the same brain. New analyses of this FuL connectomics dataset will allow tests of many theoretical predictions, but critical information on whether synaptic connections are excitatory or inhibitory is missing. The team will therefore use the Japanese Lab’s Function-Guided Inducible Morphological Analysis (FuGIMA) tool to characterize the functional response properties and morphologies of excitatory and inhibitory neurons in the pretectum. They will use these results to estimate synapse signs by the neurotransmitter type of each neuron in the FuL connectomics dataset. This proposal uses tightly integrated feedback loops between theory and experiment to address central neuroscience problems that are otherwise inaccessible. It illustrates a paradigm for extracting theoretical understanding from big data and will encourage theorists to embrace synapse-resolution systems neuroscience. 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.

NSF

Unlike artificial intelligence systems, humans are capable of flexibly adapting to their environment, producing novel goal-directed behaviors that are appropriate for a situation. For example, with the goal of doing well on an exam, a student might enact some effective study strategies, while also avoiding distraction from things like social media. Psychologists and neuroscientists have called the set of processes that support this capacity “cognitive control.” This project aims to develop and test a novel computational theory of how cognitive control works in the human brain, built using recurrent neural networks that can learn about the demands of different tasks as well as how to allocate control to rapidly improve performance on a specific task. A deeper understanding of the neural computations that underlying cognitive control has several broader impacts. First, many disorders of the brain have been characterized by failures of cognitive control, and this work helps us understand these disorders better. Second, a computational understanding of how the human brain produces flexible, goal-directed behavior will help to design next generation artificial intelligence systems. Finally, this project provides extensive outreach about topics related to cognitive control, neuroscience, and computational modeling, from classroom visits to elementary schools to workshops open to a variety of stakeholders in the medical and education communities. Technically, the project relies on an existing large set of electroencephalography (EEG) and behavioral data collected from the flanker task, a classic test of cognitive control. Recurrent neural network (RNN) models are trained to perform the task, using different RNN architectures that allow for the discovery of latent units that can learn task demands and control processes. The RNNs and human participants are tested on untrained tasks to determine whether the model generalizes to unseen human performance, and both the model and the human brain are perturbed – the model by manipulating the latent units and the human by applying transcranial ultrasound stimulation to the posterior medial frontal cortex, to determine whether the two systems break down in similar ways with damage. The findings informs the development of next-generation AI systems that could incorporate these latent units, allowing future AI systems to show more flexibility and goal-directed behavior than current systems are capable of doing. 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.

NSF

This project aims to explore how the brain processes time when looking at images, with important implications for understanding memory, learning, and visual perception. The investigators have discovered that memorable images - those more likely to be remembered later - are also perceived as lasting longer in time than forgettable ones. This finding suggests that the perception of time relates to the gathering and remembering of visual information. The research team will conduct experiments using brain imaging, eye tracking, and computer modeling to understand how this process works across different parts of the visual system. This work could lead to better treatments for conditions like Alzheimer's disease, autism, and schizophrenia, where both time perception and visual memory are disrupted. The project will also advance artificial intelligence by creating new computer models that process images more like humans do, incorporating time as a feature that current AI systems ignore. The researchers intend to develop a public database of images with time perception information that can train AI systems to predict how long visual scenes will be remembered by humans, potentially improving a wide range of AI tools including educational software that adapts to how students process visual information. Technically, the project is designed to investigate whether memorable images undergo prioritized processing that dynamically extends temporal windows to maximize information extraction. Using behavioral experiments, electroencephalography (EEG), functional magnetic resonance imaging (fMRI), eye tracking, and recurrent convolutional neural network models, the project intends to pursue three main objectives: determining the extent of memorability's influence on time perception across different image features, mapping the neural mechanisms underlying this interaction throughout the visual hierarchy, and testing whether manipulating time perception can causally alter memorability. The experimental approach includes temporal bisection and reproduction tasks using images from established memorability databases, combined with multimodal neuroimaging to track spatiotemporal dynamics from early visual areas to higher-level timing regions. The project aims to use using recurrent neural networks to predict both memorability and perceived duration, testing the hypothesis that faster processing speeds in neural representations correspond to time dilation effects. The findings informs the development of next-generation AI systems that incorporate temporal processing as a core feature for processing visual information, addressing a critical gap in current computer vision models. 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.

NEI - National Eye Institute

PROJECT SUMMARY (See instructions): We live in diverse environments featuring dynamic, interacting stimuli that can be viewed from an arbitrary set of perspectives and approached under a variety of goal scenarios. However, modern cognitive and visual neuroscience studies often stand in stark contrast to this reality of our lived experience: typically, participants view isolated, simplistic stimuli while brain activation is recorded and reconstructed or decoded neural representations are quantified. Reconstructing naturalistic visual images from fMRI data presents a challenging task: existing approaches require dozens of hours of 7T fMRI data per participant and extensive compute capability, each of which render these methods inaccessible to traditional cognitive neuroscience labs. Our long-term goal is to develop data-efficient methods to enable use of natural stimuli in cognitive neuroscience studies which characterize neural information content and transformations with cognitive operations. Our objective in the present proposal, which is the next step towards our long-term goal, is to demonstrate the efficacy of natural image reconstruction as an experimental tool in cognitive neuroscience via implementing biologically-inspired optimizations to existing approaches and benchmarking their performance on newly-acquired cognitive task data. Our work involves 3 specific aims: (1) maximize data and computational efficiency of image reconstruction approaches, (2) improve image reconstruction using neuroscience principles, and (3) investigate the impact of cognitive tasks on reconstructions from neural representations of natural images. Across all Aims, we will implement and optimize a novel Al-based image reconstruction approach applied to fMRI data. In Aim 1, we will focus on establishing the efficacy of our approach for training decoding models for new participants using minimal new fMRI data. In Aim 2, we will augment our method by incorporating principles from visual neuroscience as informative priors, like spatial receptive fields estimated from retinotopic mapping and feature-selectivity estimated from functional localizers. In Aim 3, we will test our approach by acquiring several benchmark cognitive neuroscience datasets measuring how reconstructed representations are impacted by visual cognitive task demands including visual attention and working memory. This work is innovative because it enables natural image reconstruction with tractable datasets in cognitive neuroscience labs. This work is significant because it will lead to a new understanding of the neural codes supporting visual perception, attention, and working memory, which are impacted in a host of neurological and psychiatric disorders.

NINDS - National Institute of Neurological Disorders and Stroke

While extensive evidence has shown the importance of whole-person approaches to physical and mental health, existing therapeutic interventions for neurological and mental disorders remain largely single-scale and segregated. The brain, in contrast, is a vastly multiscale and interconnected complex system, and neural dynamics transform dramatically across spatiotemporal scales, internal states, and external contexts. In particular, numerous bodies of neuroscience research have discovered methods for describing brain (dys)function in the time domain, whereas many parallel, largely disconnected efforts have discovered ways to characterize neural dynamics in the frequency domain. The goal of this project is to integrate the complementary dimensions of time and frequency into a unifying theory through the development of causal discovery methods across broad spatiotemporal scales, internal states, and external contexts. Based on extensive preliminary data, our central hypothesis (CH) is that causal mechanisms in complex networked systems such as the brain can be discovered more accurately and robustly in the frequency domain under (both of) two conditions: sufficient spatiotemporal averaging and sufficient stationarity of underlying dynamics. We test our CH through three Specific Aims, each of which combines formal mathematical analysis, causal algorithm design, large-scale numerical simulations, and in vivo electrophysiology and calcium imaging. Aim 1 tests the working hypothesis (WH) that stationary macroscopic causal mechanisms can be learned more robustly and accurately in the frequency domain. Aim 2 then tests the WH that mesoscopic causal mechanisms can best be understood using a novel form of integrated time-frequency analysis that avoids using conventional, Fourier-based decompositions, while Aim 3 tests the WH that macroscopic causal network mechanisms can be more accurately discovered using time-domain methods during the transient period following extrinsic events. The proposed research is thus highly synergistic with the NCCIH Strategic Plan, especially “Objective 1: Advance fundamental science and methods development”. Through the development of innovative causal discovery methods, the proposed research provides the computational theory needed to identify optimal intervention targets across scales and conditions. This project pioneers a transition from conventional, single-scale to a causal, systems understanding of neural information processing. Consequently, it will lay a rigorous foundation for much-needed holistic and multi-scale intervention design for neurological and mental disorders. RELEVANCE (See instructions): This project is relevant to public health because it develops an integrated framework for determining (1) where (at what nodes) and (2) how (in what “language”) to intervene for brain-related disorders. We achieve (1) by developing methods for identifying causal network mechanisms across broad spatiotemporal scales. We achieve (2) by developing a complex-systems theory to determine which analysis domain—time or frequency—is most appropriate for answering which sets of questions, at what scale, and why.

NEI - National Eye Institute

Primates—including humans and macaques—make rapid, instinctive eye movements to explore the visual world, prioritize information, and guide future actions. This preferential viewing behavior is essential for perception, social interaction, and decision-making, yet the underlying neural and computational mechanisms remain poorly understood. This is because as a natural behavior, preferential viewing comprise multiple concepts such as saliency, recognition, memory, and motor planning, which are often studied separately. Here, we propose to study preferential viewing as an integrated behavior using behavioral assays in macaques, large-scale neurophysiology, closed-loop image optimization with generative networks, and computational modeling. Our central hypothesis is that visual preferences in primates are shaped by local image features—rather than full object recognition—and are supported by activity in ventral cortical areas and the thalamus. In Aim 1, we will determine whether preferential viewing is guided by local features or global object configurations using synthesized and modified images. In Aim 2, we will identify the neural representations that predict and drive preferential viewing by recording from visual cortical areas V4, IT, and the pulvinar. In Aim 3, we will develop deep learning models trained to reproduce both gaze behavior and neuronal response patterns, with a focus on generalization to social and ecological stimuli. This project will define the neuronal and algorithmic basis of visual prioritization, advancing our understanding of primate vision and informing the next generation of biologically inspired AI. Insights from this work may lead to new applications in machine vision, social robotics, and computational psychiatry. RELEVANCE (See instructions): This project seeks to understand how primates prioritize what they look at in complex visual scenes. By combining brain recordings, behavioral testing, and deep learning models, we are working to define the neural and computational rules that guide natural eye movements. The findings could help improve artificial intelligence systems and inform tools for detecting social processing deficits in conditions like autism.

NEI - National Eye Institute

Primates—including humans and macaques—make rapid, instinctive eye movements to explore the visual world, prioritize information, and guide future actions. This preferential viewing behavior is essential for perception, social interaction, and decision-making, yet the underlying neural and computational mechanisms remain poorly understood. This is because as a natural behavior, preferential viewing comprise multiple concepts such as saliency, recognition, memory, and motor planning, which are often studied separately. Here, we propose to study preferential viewing as an integrated behavior using behavioral assays in macaques, large-scale neurophysiology, closed-loop image optimization with generative networks, and computational modeling. Our central hypothesis is that visual preferences in primates are shaped by local image features—rather than full object recognition—and are supported by activity in ventral cortical areas and the thalamus. In Aim 1, we will determine whether preferential viewing is guided by local features or global object configurations using synthesized and modified images. In Aim 2, we will identify the neural representations that predict and drive preferential viewing by recording from visual cortical areas V4, IT, and the pulvinar. In Aim 3, we will develop deep learning models trained to reproduce both gaze behavior and neuronal response patterns, with a focus on generalization to social and ecological stimuli. This project will define the neuronal and algorithmic basis of visual prioritization, advancing our understanding of primate vision and informing the next generation of biologically inspired AI. Insights from this work may lead to new applications in machine vision, social robotics, and computational psychiatry. RELEVANCE (See instructions): This project seeks to understand how primates prioritize what they look at in complex visual scenes. By combining brain recordings, behavioral testing, and deep learning models, we are working to define the neural and computational rules that guide natural eye movements. The findings could help improve artificial intelligence systems and inform tools for detecting social processing deficits in conditions like autism.

NINDS - National Institute of Neurological Disorders and Stroke

RELEVANCE (See instructions):

NIA - National Institute on Aging

PROJECT SUMMARY (See instructions): The ultimate goal of this research is to understand how brain rhythms during Non-Rapid Eye Movement (NREM) sleep relate to the replay of information encoded in the hippocampus, the subsequent transfer of that information into cortical long-term memory, and how these processes are modulated by the Locus Coeruleus (LC). Interplay during NREM sleep between cortical slow oscillations (SOs), thalamic spindles, and hippocampal sharp wave-ripples (SWRs) is crucial for transferring information into long-term memory. Both normal aging and Alzheimer's disease (AD) alter temporal properties of the dominant NREM sleep rhythms: sos, spindles, and SWRs. A key discovery in recent years is that these innate brain rhythms can be modulated by neuromodulatory centers, especially the Locus Coeruleus (LC). While recent studies, including those by the Pis of this project, have advanced our understanding of how the dynamic interplay between the cortex, thalamus, and hippocampus facilitates information transfer and memory consolidation, many essential questions about the LC's role in orchestrating thalamo-cortico-hippocampal activity during NREM sleep and its influence on sleep-dependent memory consolidation remain. We aim to tackle these questions by developing computational models tested and refined through electrophysiological and behavioral experiments in rats, along with spatially and temporally specific activation of LC neurons. Our central hypothesis is that memory consolidation processes are orchestrated by the precise timing of slow oscillations, spindles, and SWRs, and that norepinephrine (NE) release from LC neuron forebrain terminals can augment this coupling. We will test these hypotheses by combining multi-site electrophysiological recordings, circuit-selective optogenetic stimulation, behavioral assessments of memory, and the development of advanced computational models. The model-guided modulation patterns of LC activity during post-learning NREM sleep will demonstrate a causal relationship between LC-NE activity and the efficiency of sleep-dependent memory consolidation. Exploring how neuromodulation influences sleep and memory could lead to the development of novel clinical tools to mitigate cognitive dysfunctions in normal aging and AD, as well as new approaches to enhance learning. Since neuromodulatory system decline or dysfunction often heralds psychiatric and neurological diseases, pinpointing neurophysiological indicators of such dysregulation could aid in early intervention.

Coventry

Creaser Park Improvements - ARC Restoration inc. roof, flooring, windows

DECD

Create a development-ready site in Windsor Center adjacent to the state's planned commuter rail station, including the relocation of the existing animal shelter.

Martin Luther King Jr. Elementary School

The Brighton community in cooperation with Seattle Works will paint a mural on the walls surrounding the basketball courts of Martin Luther King Jr. Elementary, located at 6725 45th Ave S, on Saturday, June 23rd. Seattle Works, a local nonprofit will assist with logistics, planning and securing additional volunteers. They will also act as the fiscal sponsor. The courts and adjacent fields are a neighborhood resource and serve as the unofficial community park.

NYC Department of Cultural Affairs

Create in Chinatown, Inc