1 A B C D Linkes Auge Rechtes Auge 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.) Linkes Auge Rechtes Auge A B C D 1

2 Their receptive field looks like this: 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: Wie sehen die rezeptiven Felder in der Retina bzw. im CGL (LGN) aus (Arrow). Weshalb können dann daraus (durch welche Verschaltung?) cortikale Felder entstehen? 2

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

Basics of Computational Neuroscience

The Interdisciplinary Nature of Computational Neuroscience What is computational neuroscience ?

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

Psychophysics (human behavioral studies): Black Box Environment Stimulus Behavior Reaction

Neurophysiology: Environment Stimulus Behavior Reaction

Theoretical/Computational Neuroscience: Environment Stimulus Behavior Reaction

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

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

Cortex: CNS Systems Areas Local Nets Neurons Synapses Molekules

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

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

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

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

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

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. Molekules Synapses Neurons Local Nets Areas Systems CNS

Orientation selectivity in V1: stimulus 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. Their receptive field looks like this: Molekules Synapses Neurons Local Nets Areas Systems CNS

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

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

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

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

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

Structure of a Neuron: At the dendrite the incoming signals arrive (incoming currents) 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 Molekules Synapses Neurons Local Nets Areas Systems CNS At the synapses are the outgoing signals transmitted onto the dendrites of the target neurons

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

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