Narrative change in psychotherapy

This thesis focuses on the processes of narrative change in psychotherapy. Previous reviews of the processes of narrative change in psychotherapy concluded that a general theory that details narrative concepts appropriate to understand psychotherapy processes, explains the dynamic processes between narratives, and how they relate to positive outcomes is needed. This thesis addresses this issue by suggesting a multi-layered model that accounts for transformations in different layers of narrative organization. Accordingly, a model was specified that considers three layers of narrative organization: a micro-layer of narrative innovations that disrupt the clients’ usual way of construct meaning from life situations (innovative moments), a meso-layer of narrative scripts that integrate these narrative innovations in narrative scripts that consolidate its transformative potential (protonarratives), and, finally, a macro-layer of clients’ life story (self-narrative). Globally, the empirical studies provided support for the conceptual plausibility of this model and to the specific hypothesis that were formulated on its basis. Our observations complement previous research that had underlined the integrative processes either by emphasizing thematic coherence or integration, by emphasizing the role of dynamicity and differentiation of narrative contents and processes. Additionally, they also contribute to expand previous accounts of narrative innovation through insights on the processes that characterize narrative innovation development across psychotherapy. These studies also emphasize the role of quantitative procedures in the study of narrative processes of change as they allow us to accommodate the complexity and dynamic properties of narrative processes.

Phase transitions and nonlinear phenomena in neuronal network models

Communication and cooperation between billions of neurons underlie the power of the brain. How do complex functions of the brain arise from its cellular constituents? How do groups of neurons self-organize into patterns of activity? These are crucial questions in neuroscience. In order to answer them, it is necessary to have solid theoretical understanding of how single neurons communicate at the microscopic level, and how cooperative activity emerges. In this thesis we aim to understand how complex collective phenomena can arise in a simple model of neuronal networks. We use a model with balanced excitation and inhibition and complex network architecture, and we develop analytical and numerical methods for describing its neuronal dynamics. We study how interaction between neurons generates various collective phenomena, such as spontaneous appearance of network oscillations and seizures, and early warnings of these transitions in neuronal networks. Within our model, we show that phase transitions separate various dynamical regimes, and we investigate the corresponding bifurcations and critical phenomena. It permits us to suggest a qualitative explanation of the Berger effect, and to investigate phenomena such as avalanches, band-pass filter, and stochastic resonance. The role of modular structure in the detection of weak signals is also discussed. Moreover, we find nonlinear excitations that can describe paroxysmal spikes observed in electroencephalograms from epileptic brains. It allows us to propose a method to predict epileptic seizures. Memory and learning are key functions of the brain. There are evidences that these processes result from dynamical changes in the structure of the brain. At the microscopic level, synaptic connections are plastic and are modified according to the dynamics of neurons. Thus, we generalize our cortical model to take into account synaptic plasticity and we show that the repertoire of dynamical regimes becomes richer. In particular, we find mixed-mode oscillations and a chaotic regime in neuronal network dynamics.