An Annotated Journey Through Modern Visual Neuroscience
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Visual Cortex in Humans 251
Author's personal copy Visual Cortex in Humans 251 Visual Cortex in Humans B A Wandell, S O Dumoulin, and A A Brewer, using fMRI, and we discuss the main features of the Stanford University, Stanford, CA, USA V1 map. We then summarize the positions and proper- ties of ten additional visual field maps. This represents ã 2009 Elsevier Ltd. All rights reserved. our current understanding of human visual field maps, although this remains an active field of investigation, with more maps likely to be discovered. Finally, we Human visua l cortex comprises 4–6 billion neurons that are organ ized into more than a dozen distinct describe theories about the functional purpose and functional areas. These areas include the gray matter organizing principles of these maps. in the occi pital lobe and extend into the temporal and parietal lobes . The locations of these areas in the The Size and Location of Human Visual intact human cortex can be identified by measuring Cortex visual field maps. The neurons within these areas have a variety of different stimulus response proper- The entirety of human cortex occupies a surface area 2 ties. We descr ibe how to measure these visual field on the order of 1000 cm and ranges between 2 and maps, their locations, and their overall organization. 4 mm in thickness. Each cubic millimeter of cortex contains approximately 50 000 neurons so that neo- We then consider how information about patterns, objects, color s, and motion is analyzed and repre- cortex in the two hemispheres contain on the order of sented in these maps. -
The Davida Teller Award Lecture, 2016 Visual Brain Development: A
Journal of Vision (2017) 17(3):26, 1–24 1 The Davida Teller Award Lecture, 2016 Visual Brain Development: A review of ‘‘Dorsal Stream Vulnerability’’—motion, mathematics, amblyopia, actions, and attention # Janette Atkinson University College London, London, UK $ Research in the Visual Development Unit on ‘‘dorsal stream vulnerability’ (DSV) arose from research in two Introduction somewhat different areas. In the first, using cortical milestones for local and global processing from our neurobiological model, we identified cerebral visual Emerging cortical function and infant cortical impairment in infants in the first year of life. In the impairment second, using photo/videorefraction in population refractive screening programs, we showed that infant The work discussed at the beginning of this review spectacle wear could reduce the incidence of strabismus arose out of the first twenty years of my research with and amblyopia, but many preschool children, who had Oliver Braddick and our team in the Visual Develop- been significantly hyperopic earlier, showed visuo-motor ment Unit in Cambridge, particularly John Wattam- and attentional deficits. This led us to compare Bell and Shirley Anker (Atkinson, 2000). We began by developing dorsal and ventral streams, using sensitivity devising new methods, both behavioral (automated to global motion and form as signatures, finding deficits forced-choice preferential looking) and electrophysio- in motion sensitivity relative to form in children with logical (steady-state VEP/VERP—Visual Evoked Po- Williams syndrome, or perinatal brain injury in tential/Visual Event Related Potential) to measure the hemiplegia or preterm birth. Later research showed that normal visual capacities of infants such as acuity and this DSV was common across many disorders, both ‘‘ ’’ contrast sensitivity, over the first years of life (Atkin- genetic and acquired, from autism to amblyopia. -
Neural Construction of Conscious Perception
Neural construction of conscious perception Thesis by Janis Karan Hesse In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy CALIFORNIA INSTITUTE OF TECHNOLOGY Pasadena, California 2020 (Defended May 28th, 2020) ii 2020 Janis Hesse ORCID: 0000-0003-0405-8632 iii ACKNOWLEDGEMENTS I'd like to thank Doris Tsao for being an incredible advisor and undepletable source of ideas, advice and inspiration, for sharing her passion for science with me, and giving me the courage to ask big questions. I am very happy about the choice of my thesis committee. Markus Meister, Ueli Rutishauser, and Ralph Adolphs contributed substantially by providing useful ideas, discussions, and criticisms throughout this thesis. I want to thank current and past members of the Tsao lab, including Varun Wadia, Nicole Schweers, Audo Flores, Pinglei Bao, Liang She, Steven Le Chang, Xueqi Cheng Shay Ohayon, Tomo Sato, Joseph Wekselblatt, Francisco Luongo, Lu Liu, Anne Martin, Jessa Alexander, Erin Koch, Jialiang Lu, Yuelin Shi, Alex Farhang, Irene Caprara, Frank Lanfranchi, Lindsay Salay, Hongsun Guo, Abriana Sustaita, and Sebastian Moeller, who have all been very willing to offer me help whenever I needed, taught me the different techniques in the lab, and gave me great comments, ideas and discussions. I would also like to note that the work on human epilepsy patients described in Chapter VI is as much of Varun Wadia's work as it is mine. I am grateful to have started my PhD with such a lovely cohort. My PhD would not have been as fun without Mason McGill, Vineet Augustine, Gabriela Tavares, and Ryan Cho. -
VISUAL NEUROSCIENCE Spring 2019
PSYC/BIBB/VLST 217 VISUAL NEUROSCIENCE Spring 2019 Spring 2019 Lecture: MWF 9-10a, Leidy Labs 10 Prerequisites: PSCY 1, BIBB 109, VLST 101, or COGS 001 Synopsis: An introduction to the scientific study of vision, with an emphasis on the biological substrate and its relation to behavior. Topics will typically include physiological optics, transduction of light, visual thresholds, color vision, anatomy and physiology of the visual pathways, and the cognitive neuroscience of vision. Instructor: Alan A. Stocker, Ph.D. Office: Goddard Labs Rm 421, 3710 Hamilton Walk Phone: (215) 573 9341 Email: [email protected] Office hour: W 12-1p Teaching Assistant: Lingqi Zhang Email: [email protected] Office hour: T 5-6p, TR 5-6p Office hour location: Goddard Labs Rm 420, 3710 Hamilton Walk Course Website (CANVAS): The course has a dedicated CANVAS site. Lecture slides, homework assignments, and reading assignments/material will be posted there. Also, check the site frequently for posted announcements and Q & As on the discussion board. In general, this is the place to go first if you have any question or are in need of any information regarding the course. Requirements: Homework1-3 posted on canvas Midterm Exam 1 February 15 (in class) Midterm Exam 2 March 29 (in class) Midterm Exam 3 April 26 (in class) Final Exam (cumulative) May 13, 12-2p Policy on homework assignments: Homework assignments are essential for a proper understanding of the material. There will be three homework assignments, meant in part as preparation for each midterm exam. Assignments and due dates will be posted on CANVAS. -
Perception, As You Make It
Perception, As You Make It Behavioral & Brain Sciences commentary on Chaz Firestone & Brian Scholl, “Cognition does not affect perception: Evaluating the evidence for ‘top-down’ effects" David W. Vinson, University of California, Merced, Drew H. Abney, University of California, Merced Dima Amso, Brown University Anthony Chemero, University of Cincinnati James E. Cutting, Cornell University Rick Dale, University of California, Merced Jonathan B. Freeman, New York University Laurie B. Feldman, The University at Albany, SUNY Karl J. Friston, University College London Shaun Gallagher, University of Memphis J. Scott Jordan, Illinois State University Liad Mudrik, Tel Aviv University Sasha Ondobaka, University College London Daniel C. Richardson, University College London Ladan Shams, University of California, Los Angeles Maggie Shiffrar, California State University, Northridge Michael J. Spivey, University of California, Merced Abstract: The main question F&S pose is whether “what and how we see is functionally independent from what and how we think, know, desire, act, etc.” We synthesize a collection of concerns from an interdisciplinary set of co-authors regarding F&S’s assumptions and appeals to intuition, resulting in their treatment of visual perception as context-free. No perceptual task takes place in a contextual vacuum. How do we know that an effect is one of perception qua perception that does not involve other cognitive contributions? Experimental instructions alone involve various cognitive factors that guide task performance (Roepstorff & Frith, 2004). Even a request to detect simple stimulus features requires participants to understand the instructions (“language, memory”), keep track of them (“working memory”), become sensitive to them (“attention”), and pick up the necessary information to become appropriately sensitive (“perception”). -
Principles of Image Representation in Visual Cortex
In: The Visual Neurosciences, L.M. Chalupa, J.S. Werner, Eds. MIT Press, 2003: 1603-1615. Principles of Image Representation in Visual Cortex Bruno A. Olshausen Center for Neuroscience, UC Davis [email protected] 1 Introduction data. Neuroscientists are interested in understand- ing how the cortex extracts certain properties of the The visual cortex is responsible for most of our con- visual environment—surfaces, objects, textures, mo- scious perception of the visual world, yet we remain tion, etc.—from the data stream coming from the largely ignorant of the principles underlying its func- retina. Similarly, engineers are interested in design- tion despite progress on many fronts of neuroscience. ing algorithms capable of extracting structure con- The principal reason for this is not a lack of data, tained in images or sound—for example, to identify but rather the absence of a solid theoretical frame- and label parts within the body from medical imag- work for motivating experiments and interpreting ing data. These problems at their core are one in the findings. The situation may be likened to trying same, and progress in one domain will likely lead to to understand how birds fly without knowledge of new insights in the other. the principles of aerodynamics: no amount of ex- The key difficulty faced by both sides is that the perimentation or measurements made on the bird core principles of pattern analysis are not well un- itself will reveal the secret. The key experiment— derstood. No amount of experimentation or techno- measuring air pressure above and below the wing as logical tinkering alone can overcome this obstacle. -
The Physiology and Computation of Pyramidal Neurons
The Physiology and Computation of Pyramidal Neurons Thesis by Adam Shai In Partial Fulfillment of the Requirements for the degree of Bioengineering CALIFORNIA INSTITUTE OF TECHNOLOGY Pasadena, California 2016 (Defended December 8, 2015) ii © 2016 Adam Shai All Rights Reserved iii ACKNOWLEDGEMENTS “Sitting in the gaudy radiance of those windows hearing the organ play and the choir sing, his mind pleasantly intoxicated from exhaustion, Daniel experienced a faint echo of what it must be like, all the time, to be Isaac Newton: a permanent ongoing epiphany, an endless immersion in lurid radiance, a drowning in light, a ringing of cosmic harmonies in the ears.” Neal Stephenson in Quicksilver Sometimes it feels as if graduate school was designed specifically to keep a certain type of soul happy. Those of us whose obsessions tend to overtake polite conversation, betraying either a slight social ineptitude or quirky personality trait, depending on who you ask, tend to find a home in the academic life. That such a setting was afforded to me requires my unrelenting thanks. I cannot think of a more joyous situation than the last six years of my life, where I was expected and able to be thinking intensely about what I wanted to think about. I came to Caltech primarily as a result of an interesting conversation with Christof Koch, about consciousness, quantum mechanics, and art, when I first visited during the winter of 2009. That he became my advisor guaranteed that I would be cared for during my graduate studies. Christof takes the calling of Doktorvater seriously. The environment of K-lab, by Christof’s example and design, was one of intense intellectual playfulness. -
Do We Know What the Early Visual System Does?
The Journal of Neuroscience, November 16, 2005 • 25(46):10577–10597 • 10577 Mini-Symposium Do We Know What the Early Visual System Does? Matteo Carandini,1 Jonathan B. Demb,2 Valerio Mante,1 David J. Tolhurst,3 Yang Dan,4 Bruno A. Olshausen,6 Jack L. Gallant,5,6 and Nicole C. Rust7 1Smith-Kettlewell Eye Research Institute, San Francisco, California 94115, 2Departments of Ophthalmology and Visual Sciences, and Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan 48105, 3Department of Physiology, University of Cambridge, Cambridge CB2 1TN, United Kingdom, Departments of 4Molecular and Cellular Biology and 5Psychology and 6Helen Wills Neuroscience Institute and School of Optometry, University of California, Berkeley, Berkeley, California 94720, and 7Center for Neural Science, New York University, New York, New York 10003 We can claim that we know what the visual system does once we can predict neural responses to arbitrary stimuli, including those seen in nature. In the early visual system, models based on one or more linear receptive fields hold promise to achieve this goal as long as the models include nonlinear mechanisms that control responsiveness, based on stimulus context and history, and take into account the nonlinearity of spike generation. These linear and nonlinear mechanisms might be the only essential determinants of the response, or alternatively, there may be additional fundamental determinants yet to be identified. Research is progressing with the goals of defining a single “standard model” for each stage of the visual pathway and testing the predictive power of these models on the responses to movies of natural scenes. -
Dynamics of Excitatory-Inhibitory Neuronal Networks With
I (X;Y) = S(X) - S(X|Y) in c ≈ p + N r V(t) = V 0 + ∫ dτZ 1(τ)I(t-τ) P(N) = 1 V= R I N! λ N e -λ www.cosyne.org R j = R = P( Ψ, υ) + Mγ (Ψ, υ) σ n D +∑ j n k D k n MAIN MEETING Salt Lake City, UT Feb 27 - Mar 2 ................................................................................................................................................................................................................. Program Summary Thursday, 27 February 4:00 pm Registration opens 5:30 pm Welcome reception 6:20 pm Opening remarks 6:30 pm Session 1: Keynote Invited speaker: Thomas Jessell 7:30 pm Poster Session I Friday, 28 February 7:30 am Breakfast 8:30 am Session 2: Circuits I: From wiring to function Invited speaker: Thomas Mrsic-Flogel; 3 accepted talks 10:30 am Session 3: Circuits II: Population recording Invited speaker: Elad Schneidman; 3 accepted talks 12:00 pm Lunch break 2:00 pm Session 4: Circuits III: Network models 5 accepted talks 3:45 pm Session 5: Navigation: From phenomenon to mechanism Invited speakers: Nachum Ulanovsky, Jeffrey Magee; 1 accepted talk 5:30 pm Dinner break 7:30 pm Poster Session II Saturday, 1 March 7:30 am Breakfast 8:30 am Session 6: Behavior I: Dissecting innate movement Invited speaker: Hopi Hoekstra; 3 accepted talks 10:30 am Session 7: Behavior II: Motor learning Invited speaker: Rui Costa; 2 accepted talks 11:45 am Lunch break 2:00 pm Session 8: Behavior III: Motor performance Invited speaker: John Krakauer; 2 accepted talks 3:45 pm Session 9: Reward: Learning and prediction Invited speaker: Yael -
Alumni Director Cover Page.Pub
Harvard University Program in Neuroscience History of Enrollment in The Program in Neuroscience July 2018 Updated each July Nicholas Spitzer, M.D./Ph.D. B.A., Harvard College Entered 1966 * Defended May 14, 1969 Advisor: David Poer A Physiological and Histological Invesgaon of the Intercellular Transfer of Small Molecules _____________ Professor of Neurobiology University of California at San Diego Eric Frank, Ph.D. B.A., Reed College Entered 1967 * Defended January 17, 1972 Advisor: Edwin J. Furshpan The Control of Facilitaon at the Neuromuscular Juncon of the Lobster _______________ Professor Emeritus of Physiology Tus University School of Medicine Albert Hudspeth, M.D./Ph.D. B.A., Harvard College Entered 1967 * Defended April 30, 1973 Advisor: David Poer Intercellular Juncons in Epithelia _______________ Professor of Neuroscience The Rockefeller University David Van Essen, Ph.D. B.S., California Instute of Technology Entered 1967 * Defended October 22, 1971 Advisor: John Nicholls Effects of an Electronic Pump on Signaling by Leech Sensory Neurons ______________ Professor of Anatomy and Neurobiology Washington University David Van Essen, Eric Frank, and Albert Hudspeth At the 50th Anniversary celebraon for the creaon of the Harvard Department of Neurobiology October 7, 2016 Richard Mains, Ph.D. Sc.B., M.S., Brown University Entered 1968 * Defended April 24, 1973 Advisor: David Poer Tissue Culture of Dissociated Primary Rat Sympathec Neurons: Studies of Growth, Neurotransmier Metabolism, and Maturaon _______________ Professor of Neuroscience University of Conneccut Health Center Peter MacLeish, Ph.D. B.E.Sc., University of Western Ontario Entered 1969 * Defended December 29, 1976 Advisor: David Poer Synapse Formaon in Cultures of Dissociated Rat Sympathec Neurons Grown on Dissociated Rat Heart Cells _______________ Professor and Director of the Neuroscience Instute Morehouse School of Medicine Peter Sargent, Ph.D. -
The Macaque Face Patch System: a Window Into Object Representation
Downloaded from symposium.cshlp.org on August 3, 2015 - Published by Cold Spring Harbor Laboratory Press The Macaque Face Patch System: A Window into Object Representation DORIS TSAO Division of Biology and Biological Engineering and Computation and Neural Systems, California Institute of Technology, Pasadena, California 91125 Correspondence: [email protected] The macaque brain contains a set of regions that show stronger fMRI activation to faces than other classes of object. This “face patch system” has provided a unique opportunity to gain insight into the organizing principles of IT cortex and to dissect the neural mechanisms underlying form perception, because the system is specialized to process one class of complex forms, and because its computational components are spatially segregated. Over the past 5 years, we have set out to exploit this system to clarify the nature of object representation in the brain through a multilevel approach combining electrophysiology, anatomy, and behavior. These experiments reveal (1) a remarkably precise connectivity of face patches to each other, (2) a functional hierarchy for representation of view-invariant identity comprising at least three distinct stages along the face patch system, and (3) the computational mechanisms used by cells in face patches to detect and recognize faces, including measurement of diagnostic local contrast features for detection and measurement of face feature values for recognition. How does the brain represent objects? This question nature of these steps remains a mystery. One major trans- had its beginnings in philosophy. Our fundamental intu- formation appears to be segmentation (i.e., organizing ition of the physical world consists of a space containing visual information into discrete pieces corresponding to objects, and philosophers starting from Plato wondered different objects) (Zhou et al. -
Functional Neuroanatomy of Intuitive Physical Inference
Functional neuroanatomy of intuitive physical inference Jason Fischera,b,c,d,1, John G. Mikhaela,b,c,e, Joshua B. Tenenbauma,b,c, and Nancy Kanwishera,b,c,1 aDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139; bMcGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139; cThe Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139; dDepartment of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21202; and eHarvard Medical School, Boston, MA 02115 Contributed by Nancy Kanwisher, June 29, 2016 (sent for review May 24, 2016; reviewed by Susan J. Hespos and Doris Tsao) To engage with the world—to understand the scene in front of us, region or family of regions essentially engaged in physical in- plan actions, and predict what will happen next—we must have an ferences and recruited more for physical inference than for intuitive grasp of the world’s physical structure and dynamics. other similarly difficult prediction or perception tasks? How do the objects in front of us rest on and support each other, Although some studies have explored the neural representation how much force would be required to move them, and how will of objects’ surface and material properties (2–4) and weights (5–7) they behave when they fall, roll, or collide? Despite the centrality or investigated the brain areas involved in explicit, textbook-style of physical inferences in daily life, little is known about the brain physical reasoning (8, 9), little is known about the cortical ma- mechanisms recruited to interpret the physical structure of a scene chinery that supports the more implicit perceptual judgments about and predict how physical events will unfold.