RECEPTIVE FIELDS and FUNCTIONAL ARCHITECTURE of MONKEY STRIATE CORTEX by D
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A Detailed Model of the Primary Visual Pathway in The
The Journal of Neuroscience, July 1991, 7 7(7): 1959-l 979 A Detailed Model of the Primary Visual Pathway in the Cat: Comparison of Afferent Excitatory and lntracortical Inhibitory Connection Schemes for Orientation Selectivity Florentin WMg6tter” and Christof Koch Computation and Neural Systems Program, California Institute of Technology, Pasadena, California 91125 In order to arrive at a quantitative understanding of the dy- two inhibitory schemes, local and circular inhibition (a weak namics of cortical neuronal networks, we simulated a de- form of cross-orientation inhibition), is in good agreement tailed model of the primary visual pathway of the adult cat. with observed receptive field properties. The specificity re- This computer model comprises a 5”~ 5” patch of the visual quired to establish these connections during development field at a retinal eccentricity of 4.5” and includes 2048 ON- is low. We propose that orientation selectivity is caused by and OFF-center retinal B-ganglion cells, 8 192 geniculate at least three different mechanisms (“eclectic” model): a X-cells, and 4096 simple cells in layer IV in area 17. The weak afferent geniculate bias, broadly tuned cross-orien- neurons are implemented as improved integrate-and-fire tation inhibition, and some iso-orientation inhibition. units. Cortical receptive fields are determined by the pattern The most surprising finding is that an isotropic connection of afferent convergence and by inhibitory intracortical con- scheme, circular inhibition, in which a cell inhibits all of its nections. Orientation columns are implemented continuously postsynaptic target cells at a distance of approximately 500 with a realistic receptive field scatter and jitter in the pre- pm, enhances orientation tuning and leads to a significant ferred orientations. -
The Neocognitron As a System for Handavritten Character Recognition: Limitations and Improvements
The Neocognitron as a System for HandAvritten Character Recognition: Limitations and Improvements David R. Lovell A thesis submitted for the degree of Doctor of Philosophy Department of Electrical and Computer Engineering University of Queensland March 14, 1994 THEUliW^^ This document was prepared using T^X and WT^^. Figures were prepared using tgif which is copyright © 1992 William Chia-Wei Cheng (william(Dcs .UCLA. edu). Graphs were produced with gnuplot which is copyright © 1991 Thomas Williams and Colin Kelley. T^ is a trademark of the American Mathematical Society. Statement of Originality The work presented in this thesis is, to the best of my knowledge and belief, original, except as acknowledged in the text, and the material has not been subnaitted, either in whole or in part, for a degree at this or any other university. David R. Lovell, March 14, 1994 Abstract This thesis is about the neocognitron, a neural network that was proposed by Fuku- shima in 1979. Inspired by Hubel and Wiesel's serial model of processing in the visual cortex, the neocognitron was initially intended as a self-organizing model of vision, however, we are concerned with the supervised version of the network, put forward by Fukushima in 1983. Through "training with a teacher", Fukushima hoped to obtain a character recognition system that was tolerant of shifts and deformations in input images. Until now though, it has not been clear whether Fukushima's ap- proach has resulted in a network that can rival the performance of other recognition systems. In the first three chapters of this thesis, the biological basis, operational principles and mathematical implementation of the supervised neocognitron are presented in detail. -
Michael Paul Stryker
BK-SFN-NEUROSCIENCE_V11-200147-Stryker.indd 372 6/19/20 2:19 PM Michael Paul Stryker BORN: Savannah, Georgia June 16, 1947 EDUCATION: Deep Springs College, Deep Springs, CA (1964–1966) University of Michigan, Ann Arbor, MI, BA (1968) Massachusetts Institute of Technology, Cambridge, MA, PhD (1975) Harvard Medical School, Boston, MA, Postdoctoral (1975–1978) APPOINTMENTS: Assistant Professor of Physiology, University of California, San Francisco (1978–1983) Associate Professor of Physiology, UCSF (1983–1987) Professor of Physiology, UCSF (1987–present) Visiting Professor of Human Anatomy, University of Oxford, England (1987–1988) Co-Director, Neuroscience Graduate Program, UCSF (1988–1994) Chairman, Department of Physiology, UCSF (1994–2005) Director, Markey Program in Biological Sciences, UCSF (1994–1996) William Francis Ganong Endowed Chair of Physiology, UCSF (1995–present) HONORS AND AWARDS (SELECTED): W. Alden Spencer Award, Columbia University (1990) Cattedra Galileiana (Galileo Galilei Chair) Scuola Normale Superiore, Italy (1993) Fellow of the American Association for the Advancement of Science (1999) Fellow of the American Academy of Arts and Sciences (2002) Member of the U.S. National Academy of Sciences (2009) Pepose Vision Sciences Award, Brandeis University (2012) RPB Stein Innovator Award, Research to Prevent Blindness (2016) Krieg Cortical Kudos Discoverer Award from the Cajal Club (2018) Disney Award for Amblyopia Research, Research to Prevent Blindness (2020) Michael Stryker’s laboratory demonstrated the role of spontaneous neural activity as distinguished from visual experience in the prenatal and postnatal development of the central visual system. He and his students created influential and biologically realistic theoretical mathematical models of cortical development. He pioneered the use of the ferret for studies of the central visual system and used this species to delineate the role of neural activity in the development of orientation selectivity and cortical columns. -
Columnar Specificity of Intrinsic Horizontal and Corticocortical Connections in Cat Visual Cortex
The Journal of Neuroscience, July 1989, g(7): 2432-2442 Columnar Specificity of Intrinsic Horizontal and Corticocortical Connections in Cat Visual Cortex Charles D. Gilbert and Torsten N. Wiesel The Rockefeller University, New York, New York 10021 A prominent and stereotypical feature of cortical circuitry in O’Leary, 194 1; Lorente de No, 1949; Lund and Boothe, 1975). the striate cortex is a plexus of long-range horizontal con- This rather comfortable agreement between anatomy and phys- nections, running for 6-6 mm parallel to the cortical surface, iology was disrupted by the discovery that cortical connections which has a clustered distribution. This is seen for both instead run for many millimeters parallel to the cortical surface, intrinsic cortical connections within a particular cortical area in seeming violation of the principles of receptive field size and and the convergent and divergent connections running be- functional architecture (Fisken et al., 1975; Gilbert and Wiesel, tween area 17 and other cortical areas. To determine if these 1979, 1983). An intriguing pattern of these connections, re- connections are related to the columnar functional architec- vealed by intracellular HRP injections, was that axon collaterals ture of cortex, we combined labeling of the horizontal con- of individual pyramidal cells were distributed in discrete clus- nections by retrograde transport of rhodamine-filled latex ters, spaced from 0.5 to 1 mm apart (Gilbert and Wiesel, 1979, microspheres (beads) and labeling of the orientation col- 1983; Martin and Whitteridge, 1984). Extracellular HRP injec- umns by 2-deoxyglucose autoradiography. We first mapped tions showed that the horizontal connections were convergent the distribution of orientation columns in a small region of as well as divergent and that cells projecting to the injection site area 17 or 16, then made a small injection of beads into the were distributed in a lattice-like pattern (Rockland and Lund, center of an orientation column of defined specificity, and 1982, 1983). -
An Emergent Model of Orientation Selectivity in Cat Visual Cortical Simple Cells
The Journal of Neuroscience, August 1995, 75(8): 5448-5465 An Emergent Model of Orientation Selectivity in Cat Visual Cortical Simple Cells David C. Sometql Sacha B. Nelson,2 and Mriganka Surl ‘Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 and *Department of Biology and Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02254 It is well known that visual cortical neurons respond vigor- thalamocortical inputs (feedforward excitation) or a combination ously to a limited range of stimulus orientations, while their of intracortical inhibition and thalamocortical convergence. primary afferent inputs, neurons in the lateral geniculate nu- However, “feedforward” and “inhibitory” models (see Fig. 1) cleus (LGN), respond well to all orientations. Mechanisms are inconsistent with key pieces of physiological data and ne- based on intracortical inhibition and/or converging thala- glect the effects of connectionsfrom intracortical excitatory neu- mocottical afferents have previously been suggested to un- rons, which provide the majority of synapsesonto cells in all derlie the generation of cortical orientation selectivity; how- cortical layers (LeVay and Gilbert, 1976; Peters and Payne, ever, these models conflict with experimental data. Here, a 1993; Ahmed et al., 1994). I:4 scale model of a 1700 pm by 200 pm region of layer IV Feedforward models suggestthat cortical neuronsobtain ori- of cat primary visual cortex (area 17) is presented to dem- entation selectivity -
1 Introduction
Self-Organization, Plasticity, and Low-level Visual Phenomena in a Laterally Connected Map Mo del of the Primary Visual Cortex Risto Miikkulainen, James A. Bednar, Yo onsuck Cho e, and Joseph Sirosh Department of Computer Sciences The UniversityofTexas at Austin, Austin, TX 78712 fristo,jb ednar,yscho e,[email protected] Abstract Based on a Hebbian adaptation pro cess, the a erent and lateral connections in the RF-LISSOM mo del organize simultaneously and co op eratively, and form structures such as those observed in the primary visual cortex. The neurons in the mo del develop lo cal receptive elds that are organized into orientation, o cular dominance, and size selectivity columns. At the same time, patterned lateral connections form b etween neurons that follow the receptive eld organization. This structure is in a continuously-adapting dynamic equilibrium with the external and intrinsic input, and can account for reorganization of the adult cortex following retinal and cortical lesions. The same learning pro cesses may b e resp onsible for a number of low-level functional phenomena such as tilt aftere ects, and combined with the leaky integrator mo del of the spiking neuron, for segmentation and binding. The mo del can also b e used to verify quantitatively the hyp othesis that the visual cortex forms a sparse, redundancy-reduced enco ding of the input, which allows it to pro cess massive amounts of visual information eciently. 1 Intro duction The primary visual cortex, like many other regions of the neo cortex, is a top ographic map, organized so that adjacent neurons resp ond to adjacent regions of the visual eld. -
Inhibition, Spike Threshold, and Stimulus Selectivity in Primary Visual Cortex
Neuron Review Inhibition, Spike Threshold, and Stimulus Selectivity in Primary Visual Cortex Nicholas J. Priebe1 and David Ferster2,* 1Section of Neurobiology, The University of Texas at Austin, 1 University Station C0920, Austin, TX 78712, USA 2Department of Neurobiology and Physiology, Northwestern University, 2205 Tech Drive, Evanston, IL 60208, USA *Correspondence: [email protected] DOI 10.1016/j.neuron.2008.02.005 Ever since Hubel and Wiesel described orientation selectivity in the visual cortex, the question of how precise selectivity emerges has been marked by considerable debate. There are essentially two views of how selec- tivity arises. Feed-forward models rely entirely on the organization of thalamocortical inputs. Feedback models rely on lateral inhibition to refine selectivity relative to a weak bias provided by thalamocortical inputs. The debate is driven by two divergent lines of evidence. On the one hand, many response properties appear to require lateral inhibition, including precise orientation and direction selectivity and crossorientation sup- pression. On the other hand, intracellular recordings have failed to find consistent evidence for lateral inhibi- tion. Here we demonstrate a resolution to this paradox. Feed-forward models incorporating the intrinsic non- linear properties of cortical neurons and feed-forward circuits (i.e., spike threshold, contrast saturation, and spike-rate rectification) can account for properties that have previously appeared to require lateral inhibition. Since Hartline described inhibition between adjacent photore- et al., 2000a; Tan et al., 2004; Wehr and Zador, 2003). In addition, ceptors in the limulus retina (Hartline, 1949), the principle of lat- inactivation of the cortical circuit (including both excitatory and eral inhibition has become deeply embedded in neuroscience. -
Neural Connections and Receptive Field Properties in the Primary
REVIEW I Neural Connections and Receptive Field Properties in the Primary Visual Cortex JOSE-MANUEL ALONSO Department of Psychology University of Connecticut Storrs, Connecticut A cubic millimeter of primary visual cortex contains about 100,000 neurons that are heavily interconnected by intrinsic and extrinsic afferents. The effort of many neuroanatomists over the past has revealed the gen- eral outline of these connections; however, their function remains a mystery. Recently, combined physio- logical and anatomical approaches are beginning to reveal the role of these connections in the generation of cortical receptive fields. A common theme emerges from all these studies: cortical connections are remarkably specific and this specificity is determined in great extent by the type of connection and the neu- ronal response properties. Feedforward connections follow relatively rigid rules of wiring selectively target- ing neurons with receptive fields matched in position and contrast polarity (thalamus → cortical layer 4) or position and orientation selectivity (layer 4 → layers 2 + 3). In contrast, horizontal connections follow more flexible rules connecting distant cells that are not retinotopically aligned and neighboring cells with differ- ent orientation preferences. These differences in connectivity may give a hint on how visual stimuli are processed in the primary visual cortex. An attractive hypothesis is that local stimuli use the highly selective feedforward inputs to reliably drive cortical neurons while background stimuli modulate -
A Neural Model of Early Vision: Contrast, Contours, Corners and Surfaces
A Neural Model of Early Vision: Contrast, Contours, Corners and Surfaces Contributions toward an Integrative Architecture of Form and Brightness Perception Thorsten Hansen A University of Ulm Faculty of Computer Science Dept. of Neural Information Processing A Neural Model of Early Vision: Contrast, Contours, Corners and Surfaces Contributions toward an Integrative Architecture of Form and Brightness Perception A Neural Model of Early Vision: Contrast, Contours, Corners and Surfaces Contributions toward an Integrative Architecture of Form and Brightness Perception Thorsten Hansen aus Leer Dissertation zur Erlangung des Doktorgrades Dr. rer. nat. 2003 A University of Ulm Faculty of Computer Science Dept. of Neural Information Processing Dekan: Prof. Dr. G¨unther Palm Erster Gutachter: Prof. Dr. Heiko Neumann Zweiter Gutachter: Prof. Dr. G¨unther Palm Tag der Promotion: 22. September 2002 Abstract The thesis is concerned with the functional modeling of information processing in early and mid- level vision. The mechanisms can be subdivided into two systems, a system for the processing of discontinuities (such as contrast, contours and corners), and a complementary system for the representation of homogeneous surface properties such as brightness. For the robust processing of oriented contrast signals, a mechanism of dominating opponent inhi- bition (DOI) is proposed and integrated into an existing nonlinear simple cell model. We demon- strate that the model with DOI can account for physiological data on luminance gradient reversal. For the processing of both natural and artificial images we show that the new mechanism results in a significant suppression of responses to noisy regions, largely independent of the noise level. This adaptive processing is further examined by a stochastic analysis and numerical evaluations. -
1 Vision III: Cortical Mechanisms of Vision First You Tell Them What You're
Vision III: Cortical mechanisms of vision Please sit where you can examine a partner. Michael E. Goldberg, M.D. First you tell them what you’re gonna tell them • The cortical visual system is composed of multiple visual areas with different functions. • V1 neurons describe object features. • The principle of columnar organization. • Two visual streams – ‘what’ and ‘how’ (or ‘where’). • MT neurons describe motion and depth (dorsal stream). • IT neurons describe objects (ventral stream). See the triangle? 1 See the white bar? See the wavy line? Which small square is darker? 2 So • Your visual system does not measure and report the exact physical nature of the visual world. • It collects some data, and makes guesses. • Optical illusions take advantage of the guessing strategies. Roughly 40% of cerebral cortex is involved in vision Remember • Receptive fields in the retina and the lateral geniculate are circular, with center – surround organization. Off surround - inhibits On center - excites 3 The striate cortex – V1 – builds more sophisticated receptive fields from these basic building blocks. Cells describe specific • Contour orientations. • Binocular interaction. • Speed and direction of motion. • Color. David Hubel and Torsten Wiesel won a Nobel Prize in 1981 for describing the properties of striate cortical neurons V1 simple cell is most responsive to an oriented line Off-response On-response 4 Orientation tuning in a V1 simple cell Spikes/second Stimulus Angle (from max) V1 complex cells are sensitive to orientation of stimuli But -
1 the Death of the Cortical Column?
Preprint to appear in Studies in History and Philosophy of Science, doi: 10.1016/j.shpsa.2020.09.010. Please quote published version. The Death of the Cortical Column? Patchwork structure and conceptual retirement in neuroscientific practice Philipp Haueis Bielefeld University Department of Philosophy Postfach 100131 D- 33501 Bielefeld Email: [email protected] Phone: +49 52 106 4585 ABSTRACT: In 1981, David Hubel and Torsten Wiesel received the Nobel Prize for their research on cortical columns—vertical bands of neurons with similar functional properties. This success led to the view that “cortical column” refers to the basic building block of the mammalian ne- ocortex. Since the 1990s, however, critics questioned this building block picture of “cortical column” and debated whether this concept is useless and should be replaced with successor concepts. This paper inquires which experimental results after 1981 challenged the building block picture and whether these challenges warrant the elimination “cortical column” from neu- roscientific discourse. I argue that the proliferation of experimental techniques led to a patch- work of locally adapted uses of the column concept. Each use refers to a different kind of cor- tical structure, rather than a neocortical building block. Once we acknowledge this diverse- kinds picture of “cortical column”, the elimination of column concept becomes unnecessary. Rather, I suggest that “cortical column” has reached conceptual retirement: although it cannot be used to identify a neocortical building block, column research is still useful as a guide and cautionary tale for ongoing research. At the same time, neuroscientists should search for alter- native concepts when studying the functional architecture of the neocortex. -
Kandel Chs. 27, 28
Back 27 Central Visual Pathways Robert H. Wurtz Eric R. Kandel THE VISUAL SYSTEM HAS THE most complex neural circuitry of all the sensory systems. The auditory nerve contains about 30,000 fibers, but the optic nerve contains over one million! Most of what we know about the functional organization of the visual system is derived from experiments similar to those used to investigate the somatic sensory system. The similarities of these systems allow us to identify general principles governing the transformation of sensory information in the brain as well as the organization and functioning of the cerebral cortex. In this chapter we describe the flow of visual information in two stages: first from the retina to the midbrain and thalamus, then from the thalamus to the primary visual cortex. We shall begin by considering how the world is projected on the retina and describe the projection of the retina to three subcortical brain areas: the pretectal region, the superior colliculus of the midbrain, and the lateral geniculate nucleus of the thalamus. We shall then examine the pathways from the lateral geniculate nucleus to the cortex, focusing on the different information conveyed by the magno- and parvocellular divisions of the visual pathways. Finally, we consider the structure and function of the initial cortical relay in the primary visual cortex in order to elucidate the first steps in the cortical processing of visual information necessary for perception. Chapter 28 then follows this visual processing from the primary visual cortex into two pathways to the parietal and temporal cortex. In examining the flow of visual information we shall see how the architecture of the cortex—specifically its modular organization—is adapted to the analysis of information for vision.