Techn. Universiẗat, Diss., 2004--München.

Techn. Hochsch., Diss., 2005--Aachen.

University, Diss., 2005--Mannheim. / Erscheinungsjahr an der Haupttitelstelle: 2004.

In this thesis inhibition as a means for competition among neurons in an unsupervised learning system is studied. In the first part of the thesis, the role of inhibition in robustness against loss of information in the form of occlusion in visual data is investigated. In the second part, inhibition as a reason for loss of information in the mathematical models of neural system is addressed. In that part, a learning rule for modeling inhibition with lowered loss of information and also a dis-inhibitory system which induces a winner-take-all mechanism are introduced. The models used in this work are unsupervised feature extractors made of biologically plausible neural networks which simulate the V1 layer of the visual cortex.

In dieser Arbeit untersuche ich der Gesichterkognition zugrundeliegende biologischen Mechanismen auf der genetischen, neuronalen und verhaltensbasierten Ebene. Die neuronale Aktivität wurde mittels ereigniskorrelierter Potenziale (EKPs) untersucht und ihre Latzenzvariabilität innerhalb der Person wurde durch eine innovative Methode, Residue Iteration Decomposition (RIDE), gemessen. Die erste Studie demonstriert die Reliabilität von RIDE für die Extraktion von Einzeltrialparametern der P3b Komponente, welche in der zweiten Studie die Basis für die Untersuchung der Innen-Subjekt-Variabilität (ISV) bei der Geschwindigkeit der Gesichterkognition bildet. Die zweite Studie untersucht individuelle Unterschiede in ISV in ihrer genetischen Variation, gemessen an der Verhaltens- und neuronalen Ebene während einer Gesichterkognitionsaufgabe. Die Ergebnisse zeigen, dass ISV nicht nur mit dem COMT Val158Met Polymorphismus zusammenhängt, sondern auch von der geforderten kognitiven Verarbeitung abhängt. Zudem ist die ISV in der Reaktionszeit teilweise durch die ISV in der Geschwindigkeit zentralkognitiver Prozesse erklärbar. Studie 3 liefert neuartige Informationen für die N1/N170 Forschung. Mit einem differentialpsychologischen Ansatz konnten wir nicht nur vorangegangene Ergebnisse zur Vorhersagekraft der N170 für individuelle Unterschiede in der Gesichterkognition replizieren, sondern auch die individuellen Unterschiede in der N170 in einen allgemeinen und einen gesichtsspezifischen Teil mit unterschiedlicher Vorhersagekraft zerlegen. Darüber hinaus konnten wir zeigen, dass top-down Modulationen der N170 unterscheidbare und qualitativ unterschiedliche Beziehungen zu Fähigkeiten der Gesichterkognition aufweisen. Insgesamt zeigen die integrierten Ergebnisse der Studien meiner Dissertation die psychologische Bedeutsamkeit der intra- und interindividuellen Variabilität in der Gesichterkognition für die Erforschung der ihr zugrundeliegenden biologischen Mechanismen. / The biological mechanisms underlying face cognition from an inter- and intra-individual variability perspective at the genetic, neural, and behavioral levels are investigated. The neural activities related to face processing are measured by event-related potentials (ERPs) and their trial-by-trial latency variability are estimated using a novel and well-established method, Residue Iteration Decomposition (RIDE). Study 1 demonstrates the reliability of RIDE in extracting single-trial parameters of the P3b component. In the Study 2, individual differences in ISV of face processing speed, measured at both behavioral and neural levels during a face processing task, are studied in their genetic variation. The results suggest that individual differences in ISV are related not only to the COMT Val158Met polymorphism, but also to the type of cognitive processing (e.g., memory domain). Moreover, we showed that ISV in reaction time can be partially explained by ISV in the speed of central cognitive processes. Furthermore, the individual differences approach in Study 3, provided valuable and novel information beyond the common group-mean approach applied in the N1/N170-related research. Based on this approach, not only we could replicate previous findings that the N170 predicts individual differences in face cognition abilities, but also we could decompose individual differences in the N170 into a domain-general and a face-specific part with different predictive powers. Moreover, we showed that top-down modulations on the N170 have separable and qualitatively different relationships to face cognition abilities. In summary, the integrated results from different studies in my dissertation demonstrate the psychological importance of the information provided by inter- and intra-individual variability in face processing in the investigation of its underlying biological mechanisms.

Groups of starlings can form impressive shapes as they travel northward together in the springtime. This is among a group of natural phenomena based on swarm behaviour. The research field of artificial intelligence in computer science, particularly the areas of robotics and image processing, has in recent decades given increasing attention to the underlying structures. The behaviour of these intelligent swarms has opened new approaches for face detection as well. G. Beni and J. Wang coined the term “swarm intelligence” to describe this type of group behaviour. In this context, intelligence describes the ability to solve complex problems. The objective of this project is to automatically find exactly one face on a photo or video material by means of swarm intelligence. The process developed for this purpose consists of a combination of various known structures, which are then adapted to the task of face detection. To illustrate the result, a 3D hat shape is placed on top of the face using an example application program.

Frankfurt (Main), University, Diss., 2008.

Dissertation--Ruhr-Universität, Bochum, 2004. / Includes bibliographical references (p. 179-187).

Invariant object recognition is maybe the most basic and fundamental property of our visual system. It is the basis of many other cognitive tasks, like motor actions and social interactions. Hence, the theoretical understanding and modeling of invariant object recognition is one of the central problems in computational neuroscience. Indeed, object recognition consists of two different tasks: classification and identification. The focus of this thesis is on object identification under the basic geometrical transformations shift, scaling, and rotation. The visual system can perform shift, size, and rotation invariant object identification. This thesis consists of two parts. In the first part, we present and investigate the VisNet model proposed by Rolls. The generalization problems of VisNet triggered our development of a new neural network model for invariant object identification. Starting point for an improved generalization behavior is the search for an operation that extracts images features that are invariant under shifts, rotations, and scalings. Extracting invariant features guarantees that an object seen once in a specific pose can be identified in any pose. We present and investigate our model in the second part of this thesis.

The automation in the aquaculture is proving to be more and more effective these days. The economic drain on the aquaculture farmers due to the high mortality of the shrimps can be reduced by ensuring the welfare of the animals. The health of shrimps can decline with even barest of changes in the conditions in the farm. This is the result of increase in stress. As shrimps are quite sensitive to the changes, even small changes can increase the stress in the animals which results in the decline of health. This severely dampens the mortality rate in the animals. Also, human interference while feeding the shrimps severely induces the stress on the shrimps and thereby affecting the shrimp’s mortality. So, to ensure the optimum efficiency of the farm, the feeding of the shrimps is made automated. The underfeeding and overfeeding also affects the growth of shrimps. To determine the right amount of food to provide for shrimps, Biomass is a very helpful parameter. The use of artificial intelligence (AI) to calculate the farm's biomass is the project's primary area of interest. This model uses the cameras mounted on top of the tank at densely populated areas. These cameras monitor the farm, and our model detects the biomass. By doing so, it is possible to estimate how much food should be distributed at that particular area. Biomass of the shrimps can be calculated with the help of the number of shrimps and the average lengths of the shrimps detected. With the reduced human interference in calculating the biomass, the health of the animals improves and thereby making the process sustainable and economical.