Linkes Auge

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1 Versuchen Sie anhand der Folie „Arrow“ die folgenden Gesichtsfeldausfälle zu erklären. Welche Hirnstruktur, bzw. Auge wurde wie geschädigt? Es hilft sich vorzustellen, daß man Nervenbahnen zerschneiden könnte/kann. (Dunkel stellt den Gesichtsfeldausfall dar.) 1 Linkes Auge Rechtes Auge A B C D

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  • Versuchen Sie anhand der Folie Arrow die folgenden Gesichtsfeldausflle zu erklren. Welche Hirnstruktur, bzw. Auge wurde wie geschdigt? Es hilft sich vorzustellen, da man Nervenbahnen zerschneiden knnte/kann. (Dunkel stellt den Gesichtsfeldausfall dar.)1

  • Erklren Sie in Worten weshalb das Modell unten Orientierungsselektivitt im Cortex erklren kann.Was ist eigentlich Orientierungsselektivitt. Was ist ein On- oder Off-subfeld? (siehe Folie Arrow aber auch sptere Vorlesungen)2Wie sehen die rezeptiven Felder in der Retina bzw. im CGL (LGN) aus (Arrow). Weshalb knnen dann daraus (durch welche Verschaltung?) cortikale Felder entstehen?

  • Erklren Sie nebenstehendes Diagram in Worten (Arrow).Wenn man mit eine Elektrode nicht exakt vertikal in den Cortex einsticht, dann mit man eine sich mit der Elektrodenposition ndernde Orientientierungsselektivitt.ndert sich diese immer kontinuierlich?Was ist ein Vortex? (siehe hier map Vorlesung)3

  • Basics of ComputationalNeuroscience

  • What is computational neuroscience ?The Interdisciplinary Nature of Computational Neuroscience

  • Neuroscience:Environment


    ReactionDifferent Approaches towards Brain and Behavior

  • Psychophysics (human behavioral studies):




  • Neurophysiology:




  • Theoretical/Computational Neuroscience:




  • Levels of information processing in the nervous system

  • CNS (Central Nervous System):

    MolekulesSynapsesNeuronsLocal NetsAreas SystemsCNS

  • Cortex:

    MolekulesSynapsesNeuronsLocal NetsAreas SystemsCNS

  • Where are things happening in the brain.

    Is the informationrepresented locally ?

  • Results from human surgery MolekulesSynapsesNeuronsLocal NetsAreas SystemsCNS

  • Results from imaging techniques There are maps in the brain

    MolekulesSynapsesNeuronsLocal NetsAreas SystemsCNS

  • Visual System:

    More than 40 areas !

    Parallel processing of pixels andimage parts

    Hierarchical Analysis of increasingly complex information

    Many lateral and feedback connectionsMolekulesSynapsesNeuronsLocal NetsAreas SystemsCNS

  • Primary visual Cortex:

    MolekulesSynapsesNeuronsLocal NetsAreas SystemsCNS

  • Retinotopic Maps in V1:

    V1 contains a retinotopic map of the visual Field. Adjacent Neurons represent adjacent regions in the retina. That particular small retinal region from which a single neuron receives its input is called the receptive field of this neuron.V1 receives information from both eyes. Alternating regions in V1 (Ocular Dominanz Columns) receive (predominantely) Input from either the left or the right eye.Each location in the cortex represents a different part of the visual scene through the activity of many neurons. Different neurons encode different aspects of the image. For example, orientation of edges, color, motion speed and direction, etc.

    V1 decomposes an image into these components.

  • Orientation selectivity in V1:

    Orientation selective neurons in V1 change their activity (i.e., their frequency for generating action potentials) depending on the orientation of a light bar projected onto the receptive Field. These Neurons, thus, represent the orientation of lines oder edges in the image.stimulus

  • Superpositioning of maps in V1:

    Thus, neurons in V1 are orientation selective. They are, however, also selective for retinal position and ocular dominance as well as for color and motion. These are called features. The neurons are therefore akin to feature-detectors.For each of these parameter there exists a topographic map.These maps co-exist and are superimposed onto each other. In this way at every location in the cortex one finds a neuron which encodes a certain feature. This principle is called full coverage.

  • Local Circuits in V1:

    Selectivity is generated by specific connectionsstimulus

  • Layers in the Cortex:

    MolekulesSynapsesNeuronsLocal NetsAreas SystemsCNS

  • Local Circuits in V1:

    LGN inputsCell typesSpiny stellatecellSmooth stellatecellCircuit

  • Considerations for a Cortex ModelInputStructure of the visual pathwayAnatomy of the CortexCell TypesConnectionsTopography of the CortexX-Y Pixel-Space and its distortionOcularity-MapOrientation-MapColorFunctional Connectivity of the cortexConnection WeightsPhysiological charateristics of the neurons

  • At the dendrite the incomingsignals arrive (incoming currents)At the soma currentare finally integrated.At the axon hillock action potentialare generated if the potential crosses the membrane thresholdThe axon transmits (transports) theaction potential to distant sitesAt the synapses are the outgoingsignals transmitted onto the dendrites of the targetneurons Structure of a Neuron:

  • Different Types of Neurons:UnipolarcellBipolarcell(Invertebrate N.)Retinal bipolar celldendriteaxonsomaSpinal motoneuronHippocampalpyramidal cellPurkinje cell of thecerebellumaxonsomadendriteDifferent Typesof Multi-polarCells

  • Cell membrane:

    K+Cl-Ion channels:Membrane - Circuit diagram: