DOCSLIB.ORG

1 Introduction

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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. In addition, neurons are resp onsive to particular features in the input, such as lines of a particular orientation or o cularity. All neurons in a vertical column in the cortex typically have the same feature preferences. Vertical groups of neurons with the same orientation preference are called orientation columns and vertical groups with the same eye preference are called o cular dominance columns. The feature preferences gradually vary across the surface of the cortex in characteristic spatial patterns that constitute the cortical maps. Cortical maps are shap ed by visual exp erience. Altering the visual environment can drastically change the organization of o cular dominance and orientation columns Hub el and Wiesel 1962, 1968, 1977, 1974. The animal is most susceptible during a critical p erio d of early life, typically a few weeks. For example, if a kitten is raised with b oth eyes sutured shut, the cortex do es not develop a normal organization, and o cular dominance and orientation columns do not form. Even if the eye is op ened after a few weeks, the animal remains blind, even though the eye and the LGN are p erfectly normal. Similarly, if kittens are raised in environments containing only vertical or horizontal contours, their ability to see other orientations su ers signi cantly. In the cortex, Psychology of Learning and Motivation, in press. most cells develop preferences for these particular orientations, and do not resp ond well to the other orientations Hirsch and Spinelli 1970; Blakemore and Co op er 1970; Blakemore and van Sluyters 1975. Such exp eriments indicate that visual inputs are crucial to form a normal cortical organization, and suggest that the cortex tunes itself to the distribution of visual inputs. The discovery byvon der Malsburg 1973; see also Amari 1980 and Grossb erg 1976 that simple computational rules could drive the development of oriented receptive elds from visual input, raised the hop e that much of the structure and development of V1 could b e understo o d in terms of very simple neuronal b ehavior. However, since then substantial new discoveries havechanged our understanding of the primary visual cortex. New evidence indicates that cells in V1 are coupled by highly-sp eci c long-range lateral connections Gilb ert et al. 1990; Gilb ert and Wiesel 1983; Schwark and Jones 1989. These connections are recipro cal and far more numerous than the a erents, and they are b elieved to have a substantial in uence on cortical activity. They grow exub erantly after birth and reach their full extent in a short p erio d. During subsequent development, they get automatically pruned into well-de ned clusters. Pruning happ ens at the same time as the a erent connections organize into top ographic maps Callaway and Katz 1990, 1991,Burkhalter et al. 1993; Katz and Callaway 1992; Luhmann et al. 1986. The nal clustered distribution corresp onds closely to the distribution of a erent connections in the map. For example, in the mature visual cortex, lateral connections primarily run b etween areas with similar resp onse prop erties, such as neurons with the same orientation or eye preference Gilb ert et al. 1990; Gilb ert and Wiesel 1989; Lowel and Singer 1992. Several observations indicate that the lateral connection structure is not de ned genetically, but dep ends on the visual input: 1 When the primary visual cortex of the cat is deprived of visual input during early development, lateral connectivity remains crude and unre ned Callaway and Katz 1991. 2 The pattern of lateral connection clusters can b e altered bychanging the input to the developing cortex. The resulting patterns re ect correlations in the input Lowel and Singer 1992. 3 In the mouse somatosensory barrel cortex, sensory deprivation by sectioning the input nerve causes drastic decreases in the extent and density of lateral connections McCasland et al. 1992. These observations suggest that the development of lateral connections, like that of a erent connections, dep ends on cortical activity driven by external input. New discoveries have also changed the notion that the a erent structures and lateral connections are essentially static after a critical p erio d of early development. Recent results show that the adult cortex can undergo signi cant, often reversible, reorganization in resp onse to various sensory and cortical manipulations such as lesions in the receptive surface and the cortex Gilb ert 1992; Kaas 1991; Merzenich et al. 1990; Kapadia et al. 1994; Pettet and Gilb ert 1992. Based on the ab ove results, a new theory of the visual cortex has started to emerge. The cortex app ears to b e a continuously-adapting structure in a dynamic equilibrium with b oth the external and intrinsic input. This equilibrium is maintained by co op erative and comp etitive lateral interactions within the cortex, mediated by lateral connections Gilb ert et al. 1990. The a erent and lateral connection patterns develop synergetically and simultaneously, based on the same un- derlying pro cess. The primary function of the a erent and lateral structures is to form a sparse, redundancy-reduced enco ding of the visual input Barlow 1972; Field 1994. By integrating infor- mation over large p ortions of the cortex, lateral connections assist in grouping simple features into p erceptual ob jects Singer et al. 1990; von der Malsburg and Singer 1988, and may b e resp onsible for low-level visual phenomena such as tilt illusions and aftere ects. The exact mechanisms of such self-organization, plasticity, and function are still not completely understo o d. Computational mo dels can play a fundamental role in this research. With the advent 2 of massively parallel computers in the last veyears, it has b ecome p ossible to simulate large numb ers of neural units and their connections. At the same time, neurobiological techniques for mapping the resp onse prop erties and connectivity of neurons have b ecome sophisticated enough to constrain and validate such mo dels. This technological con uence provides a timely opp ortunity to test hyp otheses ab out cortical mechanisms through large-scale computational exp eriments. One of the most p owerful computational abstractions of biological learning is the Hebb rule Gustafsson and Wigstrom 1988; Hebb 1949, where synaptic ecacies are adjusted based on coin- cident pre- and p ostsynaptic activity.Iftwo neurons are active at the same time, their connection is deemed useful and is strengthened. Hebbian learning is usually coupled with normalization so that the ecacies do not grow without b ounds but only their relative strengths change Miller 1994b. A network of such units and connections may develop a globally ordered structure where units represent sp eci c inputs, such as lines of di erent orientation. Such a pro cess is called self- organization: there is no global sup ervisor directing the pro cess, but learning is based on a lo cal rule and driven by the input. Several computational mo dels have already shown how receptive elds and their global organi- zation in the cortical network can develop through Hebbian self-organization of a erent synapses Erwin et al. 1995; Go o dhill 1993; Kohonen 1982; Miller 1994a; Miller et al. 1989; Ob ermayer et al. 1990b; von der Malsburg 1973. Some of these mo dels have also shown that asp ects of cortical plasticity, such as remapping of cortical top ography following p eripheral lesions, can b e explained with similar mechanisms Ob ermayer et al. 1990a; Ritter et al. 1992. However, these mo dels have not taken the lateral interactions b etween cells into account, or have assumed that they are pre- set and xed and have a regular pro le. Therefore, the simultaneous self-organization of lateral connections and a erent structures, and many asp ects of cortical plasticity such as reorganization of the map in resp onse to cortical lesions and reshaping of mature receptive elds in resp onse to retinal lesions, cannot b e explained by these mo dels.
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 1 Receptive Fields and Maps in the Visual Cortex: Models of Ocular
    Receptive Fields and Maps in the Visual Cortex Mo dels of Ocular Dominance and Orientation Columns Kenneth D Miller Published in Mo dels of Neural Networks IIIEDomanyJLvan Hemmen and K Schulten Eds SpringerVerlag NY pp Anearlier and briefer version of this article appeared in The Handbook of Neural Networks MAArbib Ed The MIT Press under the ti tle Models of Ocular Dominance and Orientation Columns Reused by permission ABSTRACT The formation of o cular dominance and orientation columns in the mammalian visual cortex is briey reviewed Correlationbased mo d els for their development are then discussed b eginning with the mo dels of Von der Malsburg For the case of semilinear mo dels mo del b ehavior is well understo o d correlations determine receptive eld structure intracor tical interactions determine pro jective eld structure and the knitting together of the two determines the cortical map This provides a ba sis for simple but powerful mo dels of o cular dominance and orientation column formation o cular dominance columns form through a correlation based comp etition b etween lefteye and righteye inputs while orientation columns can form through a comp etition between ONcenter and OFF center inputs These mo dels accountwell for receptive eld structure but are not completely adequate to account for the details of cortical map struc ture Alternative approaches to map structure including the selforganizing feature map of Kohonen are discussed Finally theories of the computa tional function of correlationbased and selforganizing rules
    [Show full text]
  • Relation of Cortical Cell Orientation Selectivity to Alignment Of
    The Journal of Neuroscience. May 1991, 17(5): 1347-1359 Relation of Cortical Cell Orientation Selectivity to Alignment of Receptive Fields of the Geniculocortical Afferents that Arborize within a Single Orientation Column in Ferret Visual Cortex Barbara Chapman, Kathleen R. Zahs, and Michael P. Stryker Department of Physiology, University of California, San Francisco, California 94143-0444 Neurons in the primary visual cortex of higher mammals are and do not exhibit marked orientation preferences.When they arranged in columns, and the neurons in each column re- first describedcortical orientation specificity, Hubel and Wiesel spond best to light-dark borders of particular orientations. (1962) proposeda model to explain how cortical cells with sim- The basis of cortical cell orientation selectivity is not known. ple-type receptive fields, which predominate in the layers of One possible mechanism would be for cortical cells to re- carnivore cortex that receive direct geniculate input, could ac- ceive input from several lateral geniculate nucleus (LGN) quire orientation selectivity from their non-orientation-selec- neurons with receptive fields that are aligned in the visual tive inputs. According to this model, a given cell in the visual field (Hubel and Wiesel, 1962). We have investigated the cortex would receive excitatory inputs from geniculate cells, the relationship between the arrangement of the receptive fields receptive fields of which are aligned in the visual field. A stim- of geniculocortical afferents and the orientation preferences ulus falling along a line of the correct orientation to excite all of cortical cells in the orientation columns to which the af- of thesegeniculate cells simultaneouslywould produce the best ferents provide visual input.
    [Show full text]
  • DAVID H. HUBEL Harvard Medical School, Department of Neurobiology, Boston, Massachusetts, U.S.A
    EVOLUTION OF IDEAS ON THE PRIMARY VISUAL CORTEX, 1955-1978: A BIASED HISTORICAL ACCOUNT Nobel lecture, 8 December 1981 by DAVID H. HUBEL Harvard Medical School, Department of Neurobiology, Boston, Massachusetts, U.S.A. INTRODUCTION In the early spring of 1958 I drove over to Baltimore from Washington, D.C., and in a cafeteria at Johns Hopkins Hospital met Stephen Kuffler and Torsten Wiesel, for a discussion that was more momentous for Torsten’s and my future than either of us could have possibly imagined. I had been at Walter Reed Army Institute of Research for three years, in the Neuropsychiatry Section headed by David Rioch, working under the supervi- sion of M.G.F. Fuortes. I began at Walter Reed by developing a tungsten microelectrode and a technique for using it to record from chronically implant- ed cats, and I had been comparing the firing of cells in the visual pathways of sleeping and waking animals. It was time for a change in my research tactics. In sleeping cats only diffuse light could reach the retina through the closed eyelids. Whether the cat was asleep or awake with eyes open, diffuse light failed to stimulate the cells in the striate cortex. In waking animals I had succeeded in activating many cells with moving spots on a screen, and had found that some cells were very selective in that they responded when a spot moved in one direction across the screen (e.g. from left to right) but not when it moved in the opposite direction (1) (Fig. 1). There were many cells that I could not influence at all.
    [Show full text]