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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