2020-2021 Seminar Series
Deconstructing ubiquitin regulation of ion channels: from mechanisms to translation
Henry M. Colecraft, PhDJohn C. Dalton Professor of Physiology and Cellular Biophysics, and Professor of Pharmacology Columbia University Please click the link below to join the webinar: https://washington.zoom.us/j/95963842895?pwd=RGxJK2V1OHpUTlhMVVFBTVpkWW85dz09 Passcode: 092420
Neurophysiological mechanisms of memory guided behavior
Nelson Spruston, PhDSenior Director of Scientific Programs Janelia Research Campus Howard Hughes Medical Institute Please click the link below to join the webinar: https://washington.zoom.us/j/98977294985?pwd=NzY3eHRCTnFLejdScVQyakRMeGFuZz09 Password: 093020
Reconciling the spatial and mnemonic views of the hippocampus
Elizabeth A. Buffalo, PhDProfessor and Interim Chair, Department of Physiology and Biophysics University of Washington School of Medicine Interim Associate Director for Research Washington National Primate Research Center https://buffalomemorylab.com/ Please click the link below to join the webinar: https://washington.zoom.us/j/96606418447?pwd=c2NuS3ZNd05BYUFzUnNDVlN1V0xIZz09
Cell-type and cell specific connectivity in mouse visual cortex
Forrest Collman, Ph.D.
Allen InstituteSeminar abstract: Mammalian cortex is striking in its diversity of layers and cell types. The structure of cortical circuits is shaped by developmental programs where specificity in synapse formation and elimination create highly specific patterns of cell type specific connectivity. The cortex is also playing a key role is encoding structural changes that reflect specific experiences and are presumably encoded in structural and molecular changes in individual neurons and the synapses between them. In order to be able more fully understand the purpose of the diverse set of components that exist in the cortex, we’d like to disentangle cortical connectivity into distinct factors that reflect both a cells type and its individual identity. Recent progress in large scale electron microscopy and semi-automated methods for tracing connections is creating an opportunity to address these questions at scale. At the Allen Institute for Brain Science we have produced several large scale cortical EM volumes and with our collaborators have begun extracting biological insights from the raw images. In this seminar I’ll briefly describe the datasets we have produced, describe some of our early findings in this area and surprises these data have shown us, and sketch out what the future opportunities are for the community to engage with these remarkable datasets. Background on team: https://alleninstitute.org/what-we-do/brain-science/news-press/articles/quest-unravel-connectome data release website: https://microns-explorer.org/ host: Adrienne Fairhall
Reverse-engineering Drosophila behavior
Swiss Federal Institute of Technology (EPFL) Director, Neuroengineering Laboratory https://ramdya-lab.epfl.chAbstract: A shared goal of neuroscience and robotics is to understand how systems can be built to move effectively through the world. However, state-of-the-art algorithms for selecting and executing limbed behaviors in robots are still quite primitive compared with those used by animals. To inform robotic control approaches, we are investigating how the fly, Drosophila melanogaster, controls complex limb movements. I will discuss how we are combining 2-photon imaging of the ventral nerve cord in behaving Drosophila with physics-based simulations and neural network modeling to uncover how flies generate flexible behaviors. Host: John Tuthill Zoom url: https://washington.zoom.us/j/99187870975
Sites of Circadian Clock Neuron Plasticity Mediate Entrainment in Drosophila.
Seminar abstract:The circadian clock allows organisms to synchronize their physiology and behavior to the daily environmental changes caused by the rotation of the Earth. Key clock neurons in the circadian timekeeping network of the Drosophila brain display daily rhythms in morphology, and such remodeling has long been considered a clock output mechanism. In this talk, I will present our work describing how specific abrogation of the sites of daily neuronal remodeling, remarkably, has no measurable effects on circadian timekeeping or on any of the major output functions of the clock neuron network. Rather, the loss of these sites of plasticity impairs input pathways and affects the animal’s ability to synchronize their circadian clock to environmental time-cues. Based on these surprising results we propose an alternative model: structural plasticity in critical circadian clock neurons is the basis for proper integration of environmental time-cues and the resetting of the circadian clock. host: Gabrielle Gutierrez
Speakers: Don Hilgemann & Michael Fine
Institute: University of Texas Southwestern Medical CenterSeminar abstract: Cells can expand their plasma membrane (PM) laterally by unfolding membrane undulations and by exocytosis. We now describe a third mechanism involving invaginations held shut by the adapter protein, dynamin2. In multiple cell types, one-half of the PM can be sequestered in this fashion. Then, when Ca activates the lipid scramblase, TMEM16F, compartments open as anionic phospholipids escape from the cytoplasmic monolayer, neutral phospholipids enter the inner monolayer, and dynamins relax. Ca influx via mechanosensitive cation channels triggers local phospholipid scrambling and compartment opening during cell swelling.
host: Gucan Dai (firstname.lastname@example.org) and William N. Zagotta (email@example.com)Recent Paper: Bricogne, C. et al. TMEM16F activation by Ca(2+) triggers plasma membrane expansion and directs PD-1 trafficking. Scientific reports 9, 619, doi:10.1038/s41598-018-37056-x (2019).
Understand The Brain Using Interpretable Machine Learning Models
Anqi Wu, Ph.D.
Postdoctoral Research Scientist, Grossman Center for the Statistics of the Mind, Columbia UniversitySeminar abstract: Computational neuroscience is a burgeoning field embracing exciting scientific questions, a deluge of data, and an imperative demand for quantitative models. These opportunities promote the advancement of data-driven machine learning methods to understand our brains deeply. In particular, my work lies in such an interdisciplinary field and spans the development of neuroscience-motivated machine learning for neural and behavioral analysis in both animal and human studies. In this talk, I will show how to incorporate neuro-tailored assumptions into probabilistic modeling to discover interpretable structures. I will first present my work on Bayesian latent models for high-dimensional multi-neuron recordings in multiple cortical areas providing intriguing insights. Next, I will introduce a structured prior that integrates prior knowledge about fMRI bold signals, enhances probabilistic decoding for fMRI analysis, and discovers interpretable brain maps. Finally, I will discuss a novel probabilistic graphical model for animal pose tracking and interpretable downstream behavioral analyses. These examples illustrate the exploitation of probabilistic models guided by neuroscience assumptions applying to diverse neural and behavioral data.
Understanding distributed cognitive computations at a multi-regional scale
Ulises Pereira, Ph.D.
Swartz Fellow in Theoretical Neuroscience
New York University, Center for NeuroscienceSeminar abstract: Neuroscience is advancing at a breakneck pace into a big data era. Science consortiums, as well as large-scale lab collaborations, are producing ever-more-precise connectomes. In parallel, high-density probes now make it possible to record in the entire mouse brain during behavior by coordinating measurements across several labs. In physics, similar large-scale collaborations have been extremely successful. However, unlike physics, in which experimental discoveries go hand-to-hand with theoretical developments, in neuroscience, theories for multi-regional neural computations are seldom being developed. In this talk, I will present our recent results on modeling distributed cognitive computations in multi-regional brain circuits. I will start by describing a whole-macaque-cortex model for investigating distributed working memory. I will describe how spatial gradients of connectivity motifs integrated with connectomic and dendritic spine count data can be used to build a network model that recapitulates landmark observations in electrophysiological recordings. Then, I will demonstrate that similar principles can be used for building a model of the mouse cortico-basal ganglia-thalamocortical system that provides a framework for studying value-based decision-making. Finally, I will briefly discuss how local-circuit models for learning attractor and sequential activity can be integrated with connectomic and genetic data to study learning at a multi-regional scale.
State-dependent cortical circuits
Yale School of Medicine, Dept. of NeuroscienceSeminar abstract: Spontaneous and sensory-evoked cortical activity is highly state-dependent, promoting the functional flexibility of cortical circuits underlying perception and cognition. Using neural recordings in combination with behavioral state monitoring, we find that arousal and motor activity have complementary roles in regulating local cortical operations, providing dynamic control of sensory encoding. These changes in encoding are linked to altered performance on perceptual tasks. Neuromodulators, such as acetylcholine, may regulate this state-dependent flexibility of cortical network function. We therefore recently developed an approach for dual mesoscopic imaging of acetylcholine release and neural activity across the entire cortical mantle in behaving mice. We find spatiotemporally heterogeneous patterns of cholinergic signaling across the cortex. Transitions between distinct behavioral states reorganize the structure of large-scale cortico-cortical networks and differentially regulate the relationship between cholinergic signals and neural activity. Together, our findings suggest dynamic state-dependent regulation of cortical network operations at the levels of both local and large-scale circuits.
host: Sweta Agrawal
Towards mathematically tractable, normative models of neural computation
Michael Buice, Ph.D.
Associate Investigator, Allen Institute for Brain Science
Affiliate Professor, Department of Applied Mathematics, University of WashingtonSeminar abstract: To achieve even a partial understanding of neural computation we must 1) define mathematically tractable descriptions of complex systems that permit definitively testable predictions and 2) relate these descriptions to measurements of real neural systems in a way that permits the comparison of competing models. I will describe first a mathematically tractable approach to developing simplified descriptions of neural systems based on their constituents. I will focus in particular on applying these methods to systems with partial measurements, in which only a fraction of neurons is measured, as is typical in systems neuroscience. I will then describe the use of the Allen Brain Observatory, a large-scale physiological data set, as a test bed for systems-level normative models of neural computation, namely task-trained artificial neural networks. Finally, I will argue for an approach to modeling neural systems that incorporates both the mathematical properties of models and the experimentally available variables.
From visual perception to decision: utilizing machine learning to probe encoding and decoding in V1
Edgar Y. Walker, Ph.D.
Postdoctoral Fellow, University of Tübingen:Seminar abstract: A central goal of visual neuroscience lies in understanding how the visual system allows us to utilize visual sensory information to guide our decision-making and behavior. We can view the process as consisting of two complementary components: encoding and decoding. The visual system encodes information about the visual world into the activities of the visual cortical populations. Subsequently, the rest of the brain decodes the visual information from the population activity and performs computations to arrive at a decision and behavior. Recent advances in recording techniques allow us to collect sensory population data with ever-increasing size and complexity. Such data, especially in combination with rich naturalistic stimuli and carefully designed behavioral tasks, holds great promise in tackling the challenging questions on both encoding and decoding fronts. However, the data’s unprecedented complexity poses a significant challenge to the more conventional population analysis techniques and calls for novel approaches to take full advantage of the data. In this talk, I will present my recent work in which I specifically applied advanced deep learning-based analyses on rich population recordings from V1 in macaques and mice to advance understanding of both encoding and decoding in visual cortical populations. On the encoding front, I developed a state-of-the-art deep neural network (DNN) model of mouse V1 responses to natural images and utilized this network to generate the most exciting image (MEI) for each neuron. The MEIs significantly deviated from the conventional linear receptive fields and Gabor-filters, and their effectiveness in driving target neurons were experimentally verified in a closed-loop experimental paradigm that we called the “inception loop”. On the decoding front, I developed a novel DNN-based technique to decode trial-by-trial uncertainty information about the sensory stimulus from macaque V1 as the monkey performed a task requiring the use of the uncertainty information. Utilizing the decoded uncertainty, I was able to critically assess competing models of uncertainty information propagation in the visual system during decision-making and provided the first population electrophysiological evidence supporting the theory of probabilistic population code, a leading theory on probabilistic computations in the brain.
Seminar Title: Structural and biochemical studies reveal principles of microtubule nucleation
Tarun Kapoor, Ph.D.Pels Family Professor
The Rockefeller UniversityLab: http://www.kapoorlab.com/ Seminar abstract: Assembly of the cell division apparatus depends on the 𝛾-tubulin ring complex (𝛾-TuRC), an essential regulator of centrosomal and non-centrosomal microtubule formation. Our structural studies of this ~2.3 MDa complex reveal how >31 proteins, including 𝛾-tubulin and GCP2-6, as well as MZT1 and an actin-like protein in a “lumenal bridge” (LB) are organized into an asymmetric cone-shape architecture. We have also biochemically reconstituted the human 𝛾-TuRC (𝛾-TuRC-GFP), a ~35S complex that nucleates microtubules in vitro. In addition, we have characterized a subcomplex, 𝛾-TuRCΔLB-GFP, which lacks MZT1 and actin. Remarkably, we find that 𝛾-TuRCΔLB-GFP nucleates microtubules in a guanine nucleotide-dependent manner and with similar kinetics as the holocomplex. Electron microscopy reveals that 𝛾-TuRC-GFP resembles the native 𝛾-TuRC architecture, while 𝛾-TuRCΔLB-GFP adopts a partial cone shape presenting only 8-10 𝛾-tubulin subunits and lacks a well-ordered lumenal bridge. Our structure-function analysis suggests that the lumenal bridge facilitates the self-assembly of regulatory interfaces around a microtubule-nucleating “core” within the 𝛾-TuRC. Together, recombinant forms of human 𝛾-TuRC, a/b-tubulin and the hetero-octameric augmin complex will help define the critical components and uncover the basic principles of microtubule formation during cell division.
host: Chip Asbury
Neural mechanisms of limb proprioception and motor control in the fruit fly
Dept Physiology & Biophysics,
University of WashingtonSeminar abstract: The ability of animals to navigate complex environments depends critically on the integration of proprioceptive information with motor commands. For example, animals (including humans) who lack proprioceptive feedback can generate coarse limb movements, but are unable to execute fine motor tasks. To understand the neural computations that occur at the interface of proprioception and movement, we study the circuits of the Drosophila ventral nerve cord (VNC), which functions like the vertebrate spinal cord to control the sensation and movement of the limbs. We use electrophysiology and optical imaging to measure neural activity, and genetic tools to label and manipulate specific circuit elements in behaving flies. We combine these data with computational modeling of neural circuits and behavior to understand how the fly nervous system senses and controls the body. Although there are obvious differences between flies and humans, many of the basic building blocks of the nervous system are remarkably similar. These similarities suggest that the principles discovered in circuits of the fruit fly will be highly relevant to sensorimotor processing in other animals. host: Beth Buffalo
Alec Smith, Ph.D.
University of Washington
“Human stem cell-based models of neuromuscular disease and their application in elucidating underlying mechanisms and screening for novel therapeutic efficacy”
Seminar abstract: Inheritable neuromuscular disorders (NMDs) arise from genetic mutations that lead to dysfunction in either the motor nerves that control voluntary muscle contraction, the sensory nerves that communicate information from peripheral tissues to the brain, the skeletal musculature itself, or a combination of these tissues. Incidence rates encompassing all NMD diagnoses are roughly 72 cases per 100,000 people, with numbers increasing with age. Such conditions can be severely debilitating, with typical symptoms being progressive muscle weakness, numbness, chronic pain, and even death. High incidence rates and a burgeoning aged population, both in the United States and abroad, underscore the dire need for novel therapeutic options designed to alleviate or reverse symptoms arising from these conditions. However, the development of new strategies for targeting NMDs is hindered by a paucity of suitable preclinical models with which to predict therapeutic efficacy in patients. The wide array of affected genes, each typically supporting a number of disease-causing mutations, and the number of cell types and tissues implicated in the various NMD pathologies makes the development of predictive models challenging. Additionally, the inherent differences between humans and other species raise questions relating to the translational and predictive ability of preclinical animal models to accurately describe clinical phenomena. Recent progress in human induced pluripotent stem cell (hiPSC)-derived muscle and neuron production raises the possibility of generating human in vitro models of neuromuscular tissues to circumvent species incompatibility issues and to facilitate analysis of multiple disease-causing mutations within a single platform. However, few cell culture assays currently exist that are capable of providing researchers with data that directly correlate with clinical measurements of NMD pathology, limiting the predictive power of such technologies. My research aims to develop new preclinical, in vitro models that facilitate assessment of functional deficits in human NMDs and enable high-throughput screening of therapeutic efficacy in ameliorating symptoms in these conditions. In this talk, I will highlight my recent work focused on the establishment of human stem cell-based models of motor neurons and skeletal muscle and their incorporation into functional assays for use in mechanistic evaluation of disease etiology and in the testing of novel therapeutics. Specifically, I will discuss an electrophysiological assay for studying Charcot Marie Tooth disease and a 3D contractile assay for modeling distal arthrogryposis syndromes. I will also provide an overview of future directions for this research that focuses on the development of accurate, functional models of the human neuromuscular junction.
host: Beth Buffalo
“Cognition and movement in Parkinson’s disease.”
Michele A. Basso, Ph.D.Stella and Vincent Coates Chair in Neuroscience Professor, Department of Psychiatry and Biobehavioral Sciences Director, Fuster Laboratory of Cognitive Neuroscience Semel Institute for Neuroscience and Human Behavior UCLA
co-hosted by:Beth Buffalo ebuffalo@uw Rachel Wong wongr2@uw
Capillary-associated microglia regulate vascular structure and function.
Assistant Professor, University of Virginia, Dept Neuroscience & Center for Brain Immunology & Glia (BIG)Seminar abstract: Microglia are brain-resident immune cells with a repertoire of functions in the developing, mature and pathological brain. Despite these known roles for microglia, the extent of their interactions with the vasculature and potential regulation of vascular physiology has been insufficiently explored. Here, we document steady-state interactions between ramified CX3CR1+ myeloid cell somata and capillaries in the brain. We confirm that these myeloid cells are bona fide microglia then give a detailed spatio-temporal characterization of these capillary-associated microglia (CAMs) comparing and contrasting them with parenchymal microglia (PCMs). Molecularly, we identify microglial-specific purinergic P2RY12 receptors as a receptor regulating CAM interactions under the control of released purines from pannexin 1 channels. Furthermore, microglial elimination triggered capillary dilation, blood flow increase, and impaired vasodilative responses. Consistent with a specific role for microglia, we find that a genetic P2RY12 deficiency as well as a genetic deficiency of pannexin 1 channels is sufficient to recapitulate these vascular impairments suggesting purines released through pannexin channels play important roles in activating microglial P2RY12 receptors to regulate neurovascular structure and function. host: Oscar Vivas
Mechanisms underlying flexible information flow across the brain
Karel Svoboda, Ph.D.
Director, Allen Institute:Abstract: Neural computation and behavior are produced by shifting configurations of multi-regional neural networks, implemented by dynamic coupling between brain regions. We study the flow of information across brain regions in the context of decision-making. Decisions are held in memory until enacted, making them vulnerable to distracting sensory input. We show that selective and dynamic gating of information flow from sensory to motor cortex protects memory from interference during decision-making. Experimental data and data-driven models together show that attractor dynamics control how neural activity percolates across cortical regions during decision making and thereby protect short-term memory from distractors. Our study provides outlines of a general mechanism underlying flexible routing of information across the brain.
host: Beth Buffalo
The Future of Brain Interfacing
Flip Sabes, PhD
Starfish Neuroscience, Professor Emeritus, UCSF Department of Physiology
Brain interfacing holds immense promise, from the treatment of neurological and neuropsychiatric disorders to controlling our mental state, from restoring lost sensory, motor or cognitive function to changing how we interact with the world around us. Academic research on such devices has made steady progress, but this progress has not yet translated into real world applications. Today, we’re seeing a new surge of industry investment in brain interfacing, from small lab-based startups to the worlds largest companies. This investment might mark an inflection point in the field, creating the momentum needed to go from promise to product. Here, I’ll present my view of present state of brain interfacing and the key challenges and opportunities, as seen through the lens of my earlier work at. UCSF and my more recent work in the startup world.