A Probabilistic Approach to the Primary Visual Cortex

What can the statistical structure of natural images teach us about the human brain? Even though the visual cortex is one of the most studied parts of the brain, surprisingly little is known about how exactly images are processed to leave us with a coherent percept of the world around us, so we can recognize a friend or drive on a crowded street without any effort. By constructing probabilistic models of natural images, the goal of this thesis is to understand the structure of the stimulus that is the raison d etre for the visual system. Following the hypothesis that the optimal processing has to be matched to the structure of that stimulus, we attempt to derive computational principles, features that the visual system should compute, and properties that cells in the visual system should have. Starting from machine learning techniques such as principal component analysis and independent component analysis we construct a variety of sta- tistical models to discover structure in natural images that can be linked to receptive field properties of neurons in primary visual cortex such as simple and complex cells. We show that by representing images with phase invariant, complex cell-like units, a better statistical description of the vi- sual environment is obtained than with linear simple cell units, and that complex cell pooling can be learned by estimating both layers of a two-layer model of natural images. We investigate how a simplified model of the processing in the retina, where adaptation and contrast normalization take place, is connected to the nat- ural stimulus statistics. Analyzing the effect that retinal gain control has on later cortical processing, we propose a novel method to perform gain control in a data-driven way. Finally we show how models like those pre- sented here can be extended to capture whole visual scenes rather than just small image patches. By using a Markov random field approach we can model images of arbitrary size, while still being able to estimate the model parameters from the data.

Reacting to Complex Threats – the Securitization in the Speeches of Anders Fogh Rasmussen based on NATO´s New Strategic Concept

The purpose of this master´s thesis is to analyze how NATO Secretary General Anders Fogh Rasmussen is trying to justify the existence of the military alliance through the use of security arguments. I am puzzled by the question: why does NATO still exist – what is NATO’s raison d'être. The New Strategic Concept (2010) forms the base for his argumentation. This thesis focuses on the security argumentation of NATO which is examined by analyzing the speeches the Secretary General. The theoretical framework of this study is based on constructivist approach to international security examining the linguistic process of securitization. Issues become securitized after Anders Fogh Rasmussen names them as threats. This thesis focuses on the securitization process relating to NATO and analyses what issues Rasmussen raises to the security agenda. Research data consists of the speeches by Anders Fogh Rasmussen. They are analyzed through J.L. Austin’s speech act taxonomy and Chaïm Perelman’s argumentation theories. The thesis will concentrate on the formulation and articulation of these threats which are considered and coined as “new threats” in contemporary international relations. I am conducting this research through the use of securitization theory. This study illustrates that the threats are constructed by NATO’s member-states in unison, but the resolutions are sounded through Rasmussen’s official speeches and transcripts. . Based on the analysis it can be concluded that Rasmussen is giving reasons for the existence of NATO. This takes place by making use of speech acts and different rhetorical techniques. The results of the analysis indicate that NATO remains an essential organization for the West and the rest of the world according to the Secretary General.