Inhalt

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   1. Summary
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  2. General introduction and discussion
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   2.1 Scientific Background
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   Visual system of the blowfly
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   Connections within the lobula plate
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   Model abstract level
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   Model optimization
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  2.2. Main projects
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   Computational principle underlying the FD1-cell’s object preference
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   Binocular integration in the circuit presynaptic to the FD1-cell
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   Functional analysis on the FD1-circuit
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  2.3. General Discussion & Conclusions
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   Predictions
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   Functional aspects
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   Operating range of the models
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   Abstraction level of the models
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   Outlook
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   2.4. References
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  3. Distributed Dendritic Processing Facilitates Object Detection: A Computational Analysis on the Visual System of the Fly.
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  3.1. Abstract
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   Background
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   Methodology / Principal Findings
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   Conclusions / Significance
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   3.2. Introduction
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  3.3. Methods
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   Constraints
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   Constraints imposed by the structure of the circuitry
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   Characteristic response properties of FD-cells
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   Components of the model
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   Input organization and receptive fields
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   Distributed dendritic interaction as a low pass filter
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   Spatial Integration
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   Function of synaptic transmission
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   Direct Pooled Inhibition Model
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   Direct Distributed Inhibition Model
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   Indirect Distributed Inhibition Model
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   Optimization
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   Algorithm
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  3.4. Results
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   Direct Pooled Inhibition (DPI)
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   Direct Distributed Inhibition (DDI)
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   Indirect Distributed Inhibition (IDI)
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   Functional Principles
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   Small field tuning based on DDI
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   Small field tuning based on IDI
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   3.5. Discussion
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   The distributed inhibition satisfies all constraints
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   Advantages of distributed processing
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   Indirect inhibition is less demanding
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   Prediction to distinguish indirect and direct inhibition electrophysiologically
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   Open problems
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   Similarity to lateral inhibition
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   3.6. Acknowledgments
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   3.7. References
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  4. Binocular integration of visual information: a model study on naturalistic optic flow processing.
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   4.1. Abstract
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   4.2. Introduction
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  4.3. Material and Methods
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   Stimulus generation and electrophysiology
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   Masks
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   Animals and electrophysiological recording
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   Data analysis
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   Models
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   Eye model and peripheral processing
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   Elementary motion detection
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   Spatially integrating elements
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   Synaptic transmission
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   Local sensitivities
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   HS models
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   H1 and Hu model
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   vCH model
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   Optimizing model parameters
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   4.4. Results
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   Responses of the vCH-cell to behaviourally generated optic flow
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   Contralateral input mediated by the H1-cell
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   Contralateral input mediated by Hu-cell
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   Ipsilateral input mediated by HS-cells
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   Modeling the contribution of input elements to the vCH-cell response
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   Model H1
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   Model HS
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   Model vCH
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   Model performance in control flight sequences
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   Interactions between different input areas
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  4.5. Discussion
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   Contribution of input elements
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   Spatial integration
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   Model abstraction level
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   Functional aspects
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   4.6. Conclusions
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   4.7. Acknowledgments
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   4.8. Literature
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  5. Neuronal encoding of object and distance information: a model study on naturalistic optic flow processing.
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   5.1. Abstract
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   5.2. Introduction
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  5.3. Methods and Material
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   Models
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   Eye model and peripheral processing
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   Elementary motion detection
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   Presynaptic elements
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   Synaptic transmission
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   Local sensitivities
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   Shunting inhibition
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   Spatial integration
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   Animals and electrophysiological recording
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   Stimuli for the model simulations
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   Trajectory
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   Naturalistic stimulation
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   Size dependence
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   Test flight ‘object’
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   Test flight ‘step’
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   Test flight ‘texture dependance’
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   Optimization
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   5.4. Results
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   Responses to naturalistic stimulation
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   Object induced response increments
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   Intersaccadic responses
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   Model predictions for specific spatial configurations of the environment
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   Test flight ‘object’
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   Texture dependence
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   Test flight ‘step’
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  5.5. Discussion
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   Object-induced behavior
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   Predictive power for naturalistic stimulation conditions
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   Object detection
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   Pattern dependent response fluctuations
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   Distance coding
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   Potential functional significance
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   Pursuit of small moving targets
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   Operating range of the model
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   5.6. Conclusions
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   5.7. Acknowledgments
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   5.8. Literature
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   6. Acknowledgments