2011.10.15.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 1 Development of Complex Curricula for Molecular Bionics and Infobionics Programs within a consortial* framework** Consortium leader PETER PAZMANY CATHOLIC UNIVERSITY Consortium members SEMMELWEIS UNIVERSITY, DIALOG CAMPUS PUBLISHER The Project has been realised with the support of the European Union and has been co-financed by the European Social Fund *** **Molekuláris bionika és Infobionika Szakok tananyagának komplex fejlesztése konzorciumi keretben ***A projekt az Európai Unió támogatásával, az Európai Szociális Alap társfinanszírozásával valósul meg. PETER PAZMANY CATHOLIC UNIVERSITY SEMMELWEIS UNIVERSITY sote_logo.jpg dk_fejlec.gif INFOBLOKK 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 2 Peter Pazmany Catholic University Faculty of Information Technology BEVEZETÉS A FUNKCIONÁLIS NEUROBIOLÓGIÁBA INTRODUCTION TO FUNCTIONAL NEUROBIOLOGY www.itk.ppke.hu By Imre Kalló Contributed by: Tamás Freund, Zsolt Liposits, Zoltán Nusser, László Acsády, Szabolcs Káli, József Haller, Zsófia Maglóczky, Nórbert Hájos, Emilia Madarász, György Karmos, Miklós Palkovits, Anita Kamondi, Lóránd Erőss, Róbert Gábriel, Kisvárdai Zoltán Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 3 www.itk.ppke.hu Visual ProcessingImre Kalló & Zoltán KisvárdayPázmány Péter Catholic University, Faculty of Information Technology I. Visual pathway and processing movement, color and contour information in the human brain. II. Structure and function of the visual cortex. III. The receptive field. Functional studies on the orientation and direction selectivity. Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 4 www.itk.ppke.hu I. Visual pathway and processing movement, color and contour information in the human brain. Exp:Developmentofimagingtechniquesgreatlyfacilitatedthestudiesonthecentralprocessingofmovement,colorandcontourinformation.Amonkeywhileobservingaspecificcircularpatternofalternatingblackandwhitesquareswasinvestigatedusingfunctionalmagneticresonanceimaging(fMRI).Thisrevealedbilateralactivatedareasinthebrainalongthevisualpathwayi.e.thelateralgeniculatebodyandvisualcorticalareas,asbothsidesofthebrainreceiveinformationfromthevisualfield. Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 5 www.itk.ppke.hu Basics of processing of the movement, colour and contour information Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 6 www.itk.ppke.hu M(magno, parasol, Y) and P(parvo, midget, X) neurons form functionally different pathways towards the visual centers of the brain J:\Graphik\gym2000\Retina1.jpg J:\Graphik\gym2000\Ganglienz25x.jpg Activity pattern Magno-phasic Parvo-tonic Types of ganglioncells Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 7 www.itk.ppke.hu Parvo-cells can see the mesh Magno-cells can see the low-contrast circle C:\eysel\vorlesungen\IGSN\Rohmaterial\Mpschophys.jpg Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 8 www.itk.ppke.hu Magno-cells participate also in shape-recognition J:\Graphik\gym2000\Pei_9.jpg J:\Graphik\gym2000\Pei_9def.jpg Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 9 www.itk.ppke.hu Fibers of Magno-and Parvo cells are relayed in the visual-thalamus (dLGN) towards Area17 (V1) C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS27_6.jpg Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 10 www.itk.ppke.hu Projection of visual information to the visual cortex (V1) is retinotopic C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS27_19.jpg C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS27_9.jpg C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS27_9.jpg Introduction to functional neurobiology: Visual processing C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS27_20.jpg 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 11 www.itk.ppke.hu Defects in the visual field is characteristic for the site and extent of injury Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 12 www.itk.ppke.hu A significant portion of the mammalian cerebral cortex is involved in the processing of visual information J:\Graphik\gym2000\Viscortices.jpg Cortical areas involved in visual processing in monkey Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 13 www.itk.ppke.hu Hierarchic wiring of visual cortical areas C:\eysel\vorlesungen\IGSN\Rohmaterial\vanEssen2.jpg 1985 1991 (Felleman and Van Essen, 1991) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 14 www.itk.ppke.hu The cerebral cortex „is built” from columnar structures (morphological and functional units) Organizzazione colonnare della corteccia cerebrale. Sono illustrate schematicamente 3 minicolonne della neocorteccia del Primate ( Macaca mulatta). Le cellule gialle sono neironi inibitori, quelle rosse o verdi sono neuroni piramidali eccitatori che trasm Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. Patterns of age-related alterations in cortical pyramidal cells. http://www.science.mcmaster.ca/Psychology/psych3j03/vision/img024.gif Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 15 www.itk.ppke.hu There are columns of eyedominance in the visual cortex of both monkeys and humans C:\eysel\vorlesungen\IGSN\Rohmaterial\ODstripes.jpg (monkey 2-deoxyglucoselabelling) (human, fMRI ) Stimulus Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 16 www.itk.ppke.hu Demonstration of columns of eyedominance in the visual cortex of monkey E:\Bilder1\IGSN\labelling1.png C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS27_16.jpg C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS27_16.jpg Trans-synaptictracing(3H-aminoacids, e.g. 3H-leucine uptake from the left eye). Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 17 www.itk.ppke.hu Cytochrome-oxidase positive columns („blobs”) in the viusal cortex of the monkey (V1). Its function is unknown…. but it marks a group of colour-sensitive cells. C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS27_15.jpg Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 18 www.itk.ppke.hu Cortical connections of Magno-and Parvo-pathways C:\Eigene Dateien\IGSN\pathwaysey.jpg Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 19 www.itk.ppke.hu Temporal and parietal cortical pathways „what” vs. „where” –perception vs. action C:\eysel\vorlesungen\IGSN\Rohmaterial\what&where.jpg Introduction to functional neurobiology: Visual processing C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS28_2.jpg 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 20 www.itk.ppke.hu The dorsal and ventral pathways communicate with each other at multiple levels Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 21 www.itk.ppke.hu Functional characteristics of the cells in the V1, V2, V3, V4 and MT areas C:\eysel\vorlesungen\IGSN\Rohmaterial\V1V2V3V4MT.jpg C:\eysel\vorlesungen\IGSN\Rohmaterial\V1V2V3V4MT.jpg C:\eysel\vorlesungen\IGSN\Rohmaterial\V1V2V3V4MT.jpg C:\eysel\vorlesungen\IGSN\Rohmaterial\V1V2V3V4MT.jpg perception of colours shape recognition perception of movement stereovision C:\eysel\vorlesungen\IGSN\Rohmaterial\deyoetab.jpg C:\eysel\vorlesungen\IGSN\Rohmaterial\V1V2V3V4MT.jpg Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 22 www.itk.ppke.hu Colour and movement stimulate different cortical areas C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS28_5.jpg Motion Area Inferior-medialarea of the occipitalcortex Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 23 www.itk.ppke.hu Neurons in the MT (V5) are motion-sensitive C:\eysel\vorlesungen\IGSN\Rohmaterial\MTcell.jpg (Tootell, Born & Hamilton 1988) Optimaldirection Response histogram Direction of random point-movement Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 24 www.itk.ppke.hu The aperture-problem C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS28_6.jpg Cells at lower levels of hierachy carry out „simple” image processing and transfer the result to cells at higher levels of hierarchy(V1-V5-MST...). Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 25 www.itk.ppke.hu Aperture-problem is solved by MT cells (Mishkin et al., 1983) C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS28_7.jpg V1 MT(V5) The RF of V1 cells is smallapertureproblem Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 26 www.itk.ppke.hu Lesion of MT (V5) results in disturbance in motion perception C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS28_9.jpg Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 27 www.itk.ppke.hu Factors determining monocular (far-field) depth vision (>30 m) C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS28_11.jpg -known dimension(2,3) -overlaying(4,5) -linearperspective (6-7,8-9) -dimensionalperspective(1,2) -tone(lighter is nearer) -movementparallaxis !! near object: fast andopposite direction far object: slowand same direction Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 28 www.itk.ppke.hu Factors determining binocular (near-field) stereoscopic vision (<30 m) C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS28_12.jpg Fixationpoint (plane) of eye Identical points of 3Dobjectsare projected to different (non-corresponding)points of the retina (binoculardisparity). What is primary, object-orstereo-recognition? Stereo-recognitionappears already inV1 (Béla Julesz) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 29 www.itk.ppke.hu Depth-(disparity) senzitive cells (MST) C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS28_13.jpg C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS28_14.jpg Temporal retina Nasal retina V1 -Magno-pathway V2 -"thick" CO band MT -motion-selectivecells MST -„near-far" cells Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 30 www.itk.ppke.hu V2 (V1) cells can detect illusory contours C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS28_16.jpg (Peterhans and von der Heydt, 1991) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 31 www.itk.ppke.hu IT (Inferior Temporal Area) cells detect shape-and colour differences C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS28_17.jpg (Felleman and Van Essen, 1991 Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 32 www.itk.ppke.hu IT cells are selective for complex shapes (e.g. face) Activitypatches Tsunoda, Yamane, Nishizaki, and Tanifuji 2001 C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS28_18.jpg Bilateral lesion of IT results inprosopagnosia. - largereceptive fields (+ central area) - frequentbinocular representation Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 33 www.itk.ppke.hu The „binding” problem and its supposed solution C:\Dokumente und Einstellungen\eysel\Eigene Dateien\temp\Vase_Illusion.jpg C:\eysel\vorlesungen\IGSN\Rohmaterial\Binding2.jpg C:\eysel\vorlesungen\IGSN\Rohmaterial\Binding3.jpg During attention: oscillationandsynchronousfiring pattern. Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 34 www.itk.ppke.hu Perception is represented by the activity of different neuronal assemblies C:\Dokumente und Einstellungen\eysel\Eigene Dateien\temp\Vase_Illusion.jpg Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 35 www.itk.ppke.hu II. Structure and function of the visual cortex. Pyramidal cell: the basic cell type Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 36 www.itk.ppke.hu Global architecture of the cerebral cortex Nissl-staining(von Economo) Motorcortex (agranular) Frontál cortex Parietál cortex Occipitál cortex (granular) Coniocortex (granular) Myelin-staining(Payne, 1990) Catvisual ctx Visual cortex Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 37 www.itk.ppke.hu Criteria for subdividing visual cortical areas 1. Cytoarchitectural, myeloarchitectural, chemoarchitectural features. 2. Specific connections to other brain regions. 3. Characteristic functional maps. 4. New receptive field characteristics. 5. Special features in processing visual information and in vision-related behaviour. Nomenclature of visual cortical areas Subdivisions according to Brodmann: Area 17, 18, 19 Subdivisions in the „new era”: V1, V2, V3 etc. MT, IT etc Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 38 www.itk.ppke.hu Classification of cell types in the visual cortex I. Cells with spinousdendrites -pyramidal cells (2-6 layers) -spinousstellatecells (4.layer) -star-pyramidal cells (4. layer) asymmetric (Gray I.type)synapse round vesicles in the axon terminal neurotransmitter: glutamate (Glu) II. Cells with smooth dendrites (no spines) diverse morphology (see below) symmetric (Gray II. type) synapse pleomorphvesicles in the axon terminals neurotransmitter: gamma-aminobutyric acid (GABA) EXCITATORY 70% INHIBITORY 20% GABA-immunostaining (cat, Area 17) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 39 www.itk.ppke.hu Types of axons and synapses in the cerebral cortex 1-79 -asymmetric (Gray's type 1) 83-100 -symmetric (Gray's type 2) The shape and density of axonterminals are characteristic for the cortical cell type. (Martin and Whitteridge, 1984) (Colonnier, 1968; Famiglietti, 1970) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 40 www.itk.ppke.hu Neurotransmitter-specific labelling of pathways 3H-D-aspartate (monkeyV1, autoradiography) Autoradiography GABA-immunostaining superficial-layers, (2-3) layer (Kisvárday et al., 1989) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 41 www.itk.ppke.hu Excitatory intracortical connections in the primary visual cortex –intracellular filling of cells with horse radish peroxidase (Gilbert, 1993) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 42 www.itk.ppke.hu Main types of excitatory neurons (cat, area 17) Pyramidal cells in the 3rd layer Spinous stellate cell in the 4th layer (Martin & Whitteridge, 1984) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 43 www.itk.ppke.hu Main types of excitatory neurons (cat, area 17) Pyramidal cells in the 5th layer Pyramidal cells in the 6th layer (Martin & Whitteridge, 1984) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 44 www.itk.ppke.hu Synaptic targets of the excitatory cells (Kisvárday et al., 1986; Ahmed et al., 1994) soma % o f p o s t s y n . t a r g e t s 20 0 40 60 80 100 d.shaft d.spine axon i.s. Pyramidal cell in L6 soma % o f p o s t s y n . t a r g e t s 20 0 40 60 80 100 d.shaft d.spine axon i.s. Pyramidal cell in L3 soma % o f p o s t s y n . t a r g e t s 20 0 40 60 80 100 d.shaft d.spine axon i.s. Spinousstellatecell in L4 Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 45 www.itk.ppke.hu Inhibitory neurons –types of neurons with smooth dendrites or partially spinous dendrites (DeFelipe, 1993) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 46 www.itk.ppke.hu Distribution of synaptic targets of the various inhibitory neurons soma % o f p o s t s y n . t a r g e t s 20 0 40 60 80 100 d.shaft d.spine axon i.s. Axo-axonic (chandelier) cell soma % o f p o s t s y n . t a r g e t s 20 0 40 60 80 100 d.shaft d.spine axon i.s. Basket cell in L5 soma % o f p o s t s y n . t a r g e t s 20 0 40 60 80 100 d.shaft d.spine axon i.s. Clutch cell in L4 soma % o f p o s t s y n . t a r g e t s 20 0 40 60 80 100 d.shaft d.spine axon i.s. Largebasket cell in L3 soma % o f p o s t s y n . t a r g e t s 20 0 40 60 80 100 d.shaft d.spine axon i.s. Dendrite-targeting cell soma % o f p o s t s y n . t a r g e t s 20 0 40 60 80 100 d.shaft d.spine axon i.s. Bitufted cell soma % o f p o s t s y n . t a r g e t s 20 0 40 60 80 100 d.shaft d.spine axon i.s. Neurogliaformcell CCK soma % o f p o s t s y n . t a r g e t s 20 0 40 60 80 100 d.shaft d.spine axon i.s. Small basket cell in L2-3 soma % o f p o s t s y n . t a r g e t s 20 0 40 60 80 100 d.shaft d.spine axon i.s. Double bouquet cell Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 47 www.itk.ppke.hu Electrophysiological characteristics of the cortical neurons CortexE1.jpg CortexE2.jpg (Thomson and Deuchars, 1997) In vitro Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 48 www.itk.ppke.hu Electrophysiological characteristics of the cortical neurons In vivo 20-70 Hz "burst" 400-800 Hz (Azouz et al. 1997; Gray and McCormick, 1996) Introduction to functional neurobiology: Visual processing Colocalization1.jpg 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 49 www.itk.ppke.hu Colocalization of neurotransmitters, neuropeptides and calcium-binding proteins in neocortical neurons Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 50 www.itk.ppke.hu Colocalization of neurotransmitters, neuropeptides and calcium-binding proteins in neocortical neurons Colocalization2.jpg Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 51 www.itk.ppke.hu Colocalization of neurotransmitters, neuropeptides and calcium-binding proteins in neocortical neurons (DeFelipe, 1993) Morphological types of chemicallyidentified neurons Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 52 www.itk.ppke.hu III. The receptive field. Functional studies on the orientation and direction selectivity. Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 53 www.itk.ppke.hu Basic feature of the primary input of the visual cortex: antagonistic „center-surround” structure Retina andthalamus C:\eysel\vorlesungen\IGSN\Rohmaterial\retinarfs.jpg Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 54 www.itk.ppke.hu Features of the receptive field show robust changes in the visual cortex C:\eysel\vorlesungen\IGSN\Rohmaterial\lgnctxori2.jpg thalamus visual cortex Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 55 www.itk.ppke.hu Theory of the formation of „simple”-typed receptive field in the primary visual cortex (Hubel and Wiesel, 1962) C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS27_12.jpg retinal ON retinal OFF cortical simple ODD EVEN C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS27_11.jpg Plotting the receptive field Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 56 www.itk.ppke.hu Theory of the formation of „complex”-typed receptive field in the primary visual cortex (Hubel and Wiesel, 1962) C:\eysel\vorlesungen\IGSN\Rohmaterial\KJS27_13.jpg End-inhibition place-invariance Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 57 www.itk.ppke.hu Structure of the receptive field in the visual cortex can be modelled with the aid of „Gabor function” DaugmanJ, 1940 1-dimensional"Gabor-function" T= number of cos cycles under the area s=SD m=centrum 2-dimensional"Gabor-function" Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 58 www.itk.ppke.hu Simple cell receptive fields SCRF1.jpg Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 59 www.itk.ppke.hu Simple cell receptive fields SCRF2.tif Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 60 www.itk.ppke.hu Mechanism of formation of orientation selectivity I. "Feed-forward" model Prediction(based on theH&W model): -the stronger theorientation selectivity("RF aspect ratio") the greater the difference will be between responses evoked by theoptimal andnull-orienitation -smalldeflection from theoptimalorientationlarge portion of the stimulus falls out of thereceptivefield The greaterthe"aspect ratio" is the more selective the cell! Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 61 www.itk.ppke.hu Mechanism of formation of orientation selectivity I. (Reid and Alonso, 1995) Spatial relation of the receptive fields of cortical„simple“ cellsandthalamic cells Bound(n=23) Unbound(n=51) (Chapman et al., 1991) Relation of distribution of receptive field andthalamicafferents GABAAreceptor agonist Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 62 www.itk.ppke.hu Contrast invariance Firing intensity ofthalamiccells changes paralel with the extent ofcontrast (100 i/s). Consequence:the responseis intensifying with nonoptimalorientation with increasing contrast the response of"simple" cell is intensifying Why?Spontaneous activity thalamiccells: 10-15 i/s 1. OFF center thalamicinput is saturating(zero i/s) 2. ON center inputevokesintensifyingnettoresponse Model prediction:Response given at high contrast to non optimal orientation can be strongerthan the response given at lowcontrast to optimal orientation(„ice-berg“ effect). Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 63 www.itk.ppke.hu Contrast invariance "Ice-berg" effect "spike-threshold" (Ferster and Miller, 2000) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 64 www.itk.ppke.hu Potential mechanisms of triggering „spike-threshold” changes 1. The „spike-threshold is independent of contrast –it can be demonstrated intracellularly 2. Frequency dependent depression of thalamic synapses -it does not play significant role (it is too slow and not sufficiently strong) 3. Contrast dependent hyperpolarization (contrast adaptation) –it is unlikely (it is absent by non-optimal orientation, its progress is slow (sec)) 4. Inhibition –push-pull inhibition or „anti-phase” inhibition ON OFF OFF "ANTI-PHASE" INHIBITION IS STRONGER THANTHE EXCITATION „Anti-phase" inhibition matches the thalamicexcitation „Anti-phase" inhibition is stronger than the thalamicexcitation Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 65 www.itk.ppke.hu Anti-phase inhibition is 2-5-fold stronger, than the thalamic excitation Neuronalnetwork: 1. Inhibitorythalamicinput (DOESN’T EXIST!!) 2. Convergence of many inhibitory cells-allorientations are represented ("push-pull" arrangement) 3. Convergenece of contrast dependent inhibitory cells -representation of identical orientation ("push-pull" arrangement) (Troyer et al., 1998) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 66 www.itk.ppke.hu Inhibitory cells (basket cells) in layer 4 “Simple”-type “Complex”-type (Hirsch et al., 2003) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 67 www.itk.ppke.hu Other features of responses, which must fit in the orientation model Phenomena: 1.) Dependent on the stimulus, the response of "simple" cells is saturating with increasing contrast (suboptimalcontrast andspatialfrequency saturate faster). 2.) Temporal progress of the response given to a stimulus is changing with increasing contrast(phaseshift). 3.) The temporal frequency tuningis changing withcontrast (with increasing contrast, the rise of the response is stronger in the highertemporal frequencyrange). 4.) Superposition of two stimuli results in smaller response than their algebraic sum, even if either of those means zero activity e.g. cross-orientation. Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 68 www.itk.ppke.hu Other features of responses, which must fit in the orientation model Normalizationmodels: normalisedresponse= -normalisation ofthalamicinput bycorticalinhibition -result: sigmoid, saturationcontrast-function (this type of inhibition contains all forms of orientation, thus it is independent oforientation, „pooled”) non-normalisedresponse non-normalisedresponse of all responses Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 69 www.itk.ppke.hu Mechanism of formation of orientation selectivity II. "Feed-back" model Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 70 www.itk.ppke.hu Basic features: -weakthalamicinput - thethalamicinput is not or weakly oriented („aspect ratio“). -crucialpoint: orientation selectivityis the result of intracortical excitation and inhibition Other features: -suprathresholdthalamic inputs are enhanced by reverberationmechanisms - the spatial pattern of the response is determined by the genuin network of the cortex - theactivated corticalpattern is independent of the contrast of the stimulus - orientationselectiviy can be much sharper than the arrangement of the thalamicafferents (greater"aspect ratio") The stronger is the cortical excitation compared to the thalamicexcitation, the narrower the orientation tuning of the necessary inhibition will be. (Somers et al., 1995) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 71 www.itk.ppke.hu The intracortical inhibition sharpens the orientation selectivity Orientation .jpg Control Bicucullin (Sillito, 1975) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 72 www.itk.ppke.hu Alternative models for the formation of orientation selectivity (Vidyasagar et al., 1996) Feed-back cross-orientation inhibition Partial spatial overlapping of excitatory and inhibitory inputs (“offset”) Feed-forward convergence “Biased“ thalamicinput Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 73 www.itk.ppke.hu Mechanism of formation of direction selectivity (DS) RF stimulus stimulus Direction selectivity is a contrast invariantfeature: DS is the same for low contrast or high contrast lines. This contradictsHubel andWiesel’s(1962) "simple" cell model. Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 74 www.itk.ppke.hu Direction selectivity changes with the speed of stimulus Cell1 Cell2 Direction selectivity is lost by this speed of the stimulus (Saul and Humphrey, 1992) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 75 www.itk.ppke.hu Space-time connection is the base of direction selectivity Space-timedomain Frequencydomain PD NPD Direction selectivityis produced by local –within receptive field-interactions. 1'' Test-stimulus Combination of inputs: inhibitoryinput reversesPD !! Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 76 www.itk.ppke.hu Space-time connection in direction selectivity One of the directions:inputs are in opposite phase (1/2 cycledifference) The other direction :inputs are in the same phase (0cycledifference) .and.mean 1/4 cycle "spatiotemporal (ST)-quadrature" Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 77 www.itk.ppke.hu Space-time connection in direction selectivity .and.= 1/4 cycle idealcase Spatial difference in receptive fields Luminence profiles of the stimulus in the preferred direction Response profiles is the preferred direction Constant stimulus-(modulated by luminence) evoked temporal difference in responses Luminence profiles of the stimulus in the NON-preferred direction Response profiles is the NON-preferred direction (Saul and Feidler, 2002) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 78 www.itk.ppke.hu Determination of direction selectivity by spatial-temporal plots of cellular activity (Cat, 4B layer-cell, stimulus: standingsinus waves, 4Hz) (Murthy et al., 1998) Fourier movingsinus- ST-inseparable Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 79 www.itk.ppke.hu Determination of direction selectivity by means of the spatial-temporal plots of cellular activity ST-separable 0<=STI<=1 Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 80 www.itk.ppke.hu Convergence model of lagged and non-lagged cells laggedcell Thalamus luminance Off-set response OFF non-lagged .=0.52 ON lagged .=0.25 ON non-lagged .= -0.05 .=0 maximum luminance non-laggedcell X-typethalamic cells (Humphrey and Saul, 2003; Mastronarde, 1987a,b) (Humphrey and Saul, 2002) Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 81 www.itk.ppke.hu Determination of temporal differences for lagged and non-lagged thalamic cells (delay inms) "X-lagged" phasedelay The crosspoints of the diagramms refer to the absolutephase "X-non-lagged„ phase precession The steepness of the diagram is in correlation with the response delay Area 17 cells: in the case of faststimulus (>2-4 Hz)thedirection selectivity is reduced or absent. Introduction to functional neurobiology: Visual processing 2011.10.15. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 82 www.itk.ppke.hu Intracortical feed-back model of direction selectivity The essence of the model: latencies are produced by the genuin network of the cortex -temporal activity of thethalamicinput is homogenous. (lagged, non-laggedcells are not disting