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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.)
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Linkes Auge Rechtes Auge
A
B
C
D
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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:
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Wie sehen die rezeptiven Felder in der Retina bzw. im CGL (LGN) aus (Arrow). Weshalb können dann daraus (durch welche Verschaltung?) cortikale Felder entstehen?
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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)
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Basics of ComputationalNeuroscience
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What is computational neuroscience ?
The Interdisciplinary Nature of Computational Neuroscience
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Neuroscience:
Environment
Stimulus
Behavior
Reaction
Different Approaches towards Brain and Behavior
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Psychophysics (human behavioral studies):
Environment
Stimulus
Behavior
Reaction
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Environment
Stimulus
Neurophysiology:
Behavior
Reaction
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Environment
Stimulus
Theoretical/Computational Neuroscience:
Behavior
Reactiondx
)(xf
U
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Levels of information processing in the nervous system
Molecules0.1m
Synapses1m
Neurons100m
Local Networks1mm
Areas / „Maps“ 1cm
Sub-Systems10cm
CNS1m
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CNS (Central Nervous System):
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
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Cortex:
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
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Where are things happening in the brain.
Is the informationrepresented locally ?
The Phrenologists viewat the brain(18th-19th centrury)
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
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Results from human surgery
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
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Results from imaging techniques – There are maps in the brain
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
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Visual System:More than 40 areas !
Parallel processing of „pixels“ andimage parts
Hierarchical Analysis of increasingly complex information
Many lateral and feedback connections
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
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Primary visual Cortex:
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
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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.
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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
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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
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Local Circuits in V1:
Selectivity is generated by specific connections
stimulus
Orientation selectivecortical simple cell
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
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Layers in the Cortex:
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
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Local Circuits in V1:
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
LGN inputs Cell types
Spiny stellatecell Smooth stellate
cell
Circuit
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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
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At the dendrite the incomingsignals arrive (incoming currents)
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
At the soma currentare finally integrated.
At the axon hillock action potentialare generated if the potential crosses the membrane threshold
The axon transmits (transports) theaction potential to distant sites
At the synapses are the outgoingsignals transmitted onto the dendrites of the targetneurons
Structure of a Neuron:
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Different Types of Neurons:
Unipolarcell
Bipolarcell
(Invertebrate N.) Retinal bipolar cell
dendrite
axon
soma
Spinal motoneuronHippocampalpyramidal cell
Purkinje cell of thecerebellum
axonsoma
dendrite
Different Typesof Multi-polarCells
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Cell membrane:
K+
Cl-
Ion channels:
Membrane - Circuit diagram:
rest