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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.

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Präsentation zum Thema: "1 Versuchen Sie anhand der Folie Arrow die folgenden Gesichtsfeldausfälle zu erklären. Welche Hirnstruktur, bzw. Auge wurde wie geschädigt? Es hilft sich."—  Präsentation transkript:

1 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 AugeRechtes Auge A B C D

2 2 Erklären Sie in Worten weshalb das Modell unten Orientierungsselektivität im Cortex erklären kann. Was ist eigentlich Orientierungsselektivität. Was ist ein On- oder Off-subfeld? (siehe Folie Arrow aber auch spätere Vorlesungen) Their receptive field looks like this: 2 Wie sehen die rezeptiven Felder in der Retina bzw. im CGL (LGN) aus (Arrow). Weshalb können dann daraus (durch welche Verschaltung?) cortikale Felder entstehen?

3 3 Erklären Sie nebenstehendes Diagram in Worten (Arrow). Wenn man mit eine Elektrode nicht exakt vertikal in den Cortex einsticht, dann mißt man eine sich mit der Elektrodenposition ändernde Orientientierungsselektivität. Ändert sich diese immer kontinuierlich? Was ist ein Vortex? (siehe hier map Vorlesung) 3

4 4 Basics of Computational Neuroscience

5 5 What is computational neuroscience ? The Interdisciplinary Nature of Computational Neuroscience

6 6 Neuroscience: Environment Stimulus Behavior Reaction Different Approaches towards Brain and Behavior

7 7 Psychophysics (human behavioral studies): Environment Stimulus Behavior Reaction

8 8 Environment Stimulus Neurophysiology: Behavior Reaction

9 9 Environment Stimulus Theoretical/Computational Neuroscience: Behavior Reaction

10 10 Levels of information processing in the nervous system Molecules 0.1 m Synapses 1 m Neurons 100 m Local Networks 1mm Areas / Maps 1cm Sub-Systems 10cm CNS 1m

11 11 CNS (Central Nervous System): Molekules Synapses Neurons Local Nets Areas Systems CNS

12 12 Cortex: Molekules Synapses Neurons Local Nets Areas Systems CNS

13 13 Where are things happening in the brain. Is the information represented locally ? The Phrenologists view at the brain (18th-19th centrury) Molekules Synapses Neurons Local Nets Areas Systems CNS

14 14 Results from human surgery Molekules Synapses Neurons Local Nets Areas Systems CNS

15 15 Results from imaging techniques – There are maps in the brain Molekules Synapses Neurons Local Nets Areas Systems CNS

16 16 Visual System: More than 40 areas ! Parallel processing of pixels and image parts Hierarchical Analysis of increasingly complex information Many lateral and feedback connections Molekules Synapses Neurons Local Nets Areas Systems CNS

17 17 Primary visual Cortex: Molekules Synapses Neurons Local Nets Areas Systems CNS

18 18 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. Molekules Synapses Neurons Local Nets Areas Systems CNS 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.

19 19 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. Molekules Synapses Neurons Local Nets Areas Systems CNS Their receptive field looks like this: stimulus

20 20 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. Molekules Synapses Neurons Local Nets Areas Systems CNS

21 21 Local Circuits in V1: Selectivity is generated by specific connections stimulus Orientation selective cortical simple cell Molekules Synapses Neurons Local Nets Areas Systems CNS

22 22 Layers in the Cortex: Molekules Synapses Neurons Local Nets Areas Systems CNS

23 23 Local Circuits in V1: Molekules Synapses Neurons Local Nets Areas Systems CNS LGN inputs Cell types Spiny stellate cell Smooth stellate cell Circuit

24 24 Considerations for a Cortex Model Input –Structure of the visual pathway Anatomy of the Cortex –Cell Types –Connections Topography of the Cortex –X-Y Pixel-Space and its distortion –Ocularity-Map –Orientation-Map –Color Functional Connectivity of the cortex –Connection Weights –Physiological charateristics of the neurons At least all these things need to be considered when making a complete cortex model

25 25 At the dendrite the incoming signals arrive (incoming currents) Molekules Synapses Neurons Local Nets Areas Systems CNS At the soma current are finally integrated. At the axon hillock action potential are generated if the potential crosses the membrane threshold The axon transmits (transports) the action potential to distant sites At the synapses are the outgoing signals transmitted onto the dendrites of the target neurons Structure of a Neuron:

26 26 Different Types of Neurons: Unipolar cell Bipolar cell (Invertebrate N.) Retinal bipolar cell dendrite axon soma Spinal motoneuron Hippocampal pyramidal cell Purkinje cell of the cerebellum axon soma dendrite Different Types of Multi-polar Cells

27 27 Cell membrane: K+K+ Cl - Ion channels: Membrane - Circuit diagram: rest


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