Pawelzik, Klaus
Lade...
Preferred name
Pawelzik, Klaus
Official Name
Pawelzik, Klaus
Alternative Name
Pawelzik, Klaus Richard
Pawelzik, Klaus R.
Pawelzik, K. R.
ORCID
GND
5 Ergebnisse
Gerade angezeigt 1 - 5 von 5
- Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Exploring the role of excitatory-inhibitory dynamics in synaptic plasticity, memory stability, and neural coding(2024-05-27); ; ; This thesis provides an exploration of the brain's neural dynamics, shedding light on the mechanisms underlying learning, memory retention, neural synchronization, and sensory processing. Through a comprehensive series of studies, we explore how neurons adjust their connections in response to specific patterns, a fundamental aspect of memory and learning. This synaptic plasticity demonstrates a tight balance between different neural adaptations, allowing the brain to retain old memories while continuously forming new ones. The Investigation proceeds with an investigation of collective neural behavior, in particular the transitions between different firing states within networks of neurons. Our results characterize the critical points that govern these transitions, contributing significantly to the understanding of large-scale neural coordination. This insight highlights the importance of maintaining a balance between excitatory and inhibitory neurons, which directly impacts the brain's ability to process information and maintain functional states. Delving deeper into the mechanisms of sensory processing, our study examines the brain's response to visual stimuli, specifically under circumstances that generate gamma oscillations in the visual cortex. The interaction between external stimuli and brain wave activity shows layer-specific effects, providing a detailed insight into the neural basis of perception and cognition. In conclusion, this thesis presents a focused view of neural function, from synaptic alterations to network dynamics and sensory processing. It highlights the brain's remarkable capacity for stability amidst constant change, a foundational aspect underpinning the continuity of memory and the adaptability of learning. The collective findings of study offer profound implications for both neuroscience and the development of advanced artificial neural systems, emphasizing the necessity of intricate balancing in both natural and artificial learning environments.Dissertation239 188 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, The role of long-range connections in contextual processing and spontaneous activity of primary visual cortexThe aim of this work is to set the basis for the development of a theoretical framework to investigate how artificial signals can be successfully introduced into primary visual cortex through electrical stimulation. This goal is approached by focusing on two different aspects of visual information processing: the contextual modulations that occur when localized visual stimuli are placed in conjunction with surround stimuli and the spontaneous activity that emerges in the absence of sensory stimulation. Generalizing the well known standard sparse coding framework, we propose a generative model to encode spatially extended visual scenes. We show that pairing an anatomically inspired constraint (which imposes that neurons have direct access only to small portions of the visual field) to a computational coding principle (whose goal is to maximize accuracy and sparseness of stimuli-representation) is sufficient to account for a number of heterogeneous features. In particular, when trained with natural images, the model predicts a connectivity structure linking neurons with similar orientation preferences matching the typical patterns found for long-ranging horizontal axons and feedback projections in visual cortex. When subjected to contextual stimuli typically used in empirical studies, it replicates several hallmark effects of surround modulation, some of which previously unexplained, and provides a uniform explanation to contextual processing. The dynamics of ongoing activity in primary visual cortex was investigated in a structurally simple model, where the network connectivity was chosen to mimic what we obtained from the optimization process in the sparse coding model. We used both analytical and numerical methods to study the patterns of activity that the model exhibited, identifying conditions under which biophysically realistic orientation-tuned states emerged. We quantified several properties important for comparing the model to experimental data, such as the emergence and decay probability, average persistence, localization and coexistence of different states. In both studies, we show to what extent the properties of long-range connections between visual cortical neurons are responsible for the observed empirical facts, proposing a well- defined functional role for horizontal axons and feedback projections for contextual processing phenomena and for the generation of spontaneous tuned states. In the last part of this thesis, we tackle more concretely the problem of inducing artificial perceptions via electrical stimulation of primary visual cortex. We present a new stimulation- paradigm which consists in monitoring the spontaneous orientation-tuned states and delivering a weak modulatory current when the cortex is in a desired state, to induce spikes in neurons that are currently close to their firing threshold. The proposed framework is tested in a structurally simple spiking neural network whose activity resembles spontaneous activity in V1. After calibrating the model to a physiologically realistic operating point, we conduct a feasibility study, investigating in particular the relations between stimulation amplitude, temporal resolution and specificity of the percept. We then show how this strategy has the potential to result in the artificial perception of an image composed by a combination of oriented features, an improvement with respect to the round phosphenes typically observed in experiments.Dissertation324 212 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Aktive Computation in offenen Systemen. Lerndynamiken in biologischen Systemen: Vom Netzwerk zum Organismus(2003-06-17); ; ; In this thesis the action-perception-cycle, which is inherent in every cognitive system, is investigated by several special examples. These examples are taken from different levels of description and are biological motivated. Moreover, a novel Hebb-like learning rule for neural networks is introduced, which has not only interesting features by itself but establishes a connection to the action-perception-cycle and demonstrates the interplay between different levels of description.Dissertation345 143 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Contour Integration Models Predicting Human Behavior(2007-08-15); ; ; Contour integration is believed to be a fundamental process inobject recognition and image segmentation. However, its neuronalmechanisms are still not well understood. Psychophysical experimentsshowed that humans are remarkably efficient in integrating contourseven if these are jittered or partially occluded. Therefore thebrain requires a reliable algorithm for extracting contours fromstimuli. Several recent publications demonstrated that the brainoften uses optimal strategies to integrate sensory information.Hence in this thesis I want to tackle the question which contourintegration model describes human contour integration best.Mathematically, contour ensembles can be characterized by aconditional link probability density between oriented edge elements,termed an association field. This association field can be used togenerate contours or vice versa to extract a contour from astimulus. While in most neuronal network models all inputs to aneuron are summed up, in such a probabilistically motivated neuralnetwork for contour integration the afferent input due to the visualstimuli and the lateral input from horizontal network interactionsare multiplied.Long-range horizontal interactions in primary visual cortex linkorientation columns with similar preferred orientations and areoftenassumed to be the neuronal substrate for the association field. Experimental findings in monkeys suggest isotropic long-rangehorizontal connections, spreading symmetrically into all directionsfrom an orientation column. In contrast, probabilistic modelsrequire unidirectional lateral interactions, linking orientationcolumns in only one direction, in order to get optimal contourdetection performance.Using stimuli generated from given association fields, our numericalsimulations show that contour detection performance for both,probabilistic-multiplicative as well as additive models reacheshuman performance. Hence detection performance alone is insufficientto rule out either model class. However, psychophysical experimentswith humans reveal that contour detection errors are not maderandomly, but are highly correlated among different subjects. Thus amodel describing contour integration in the brain should not onlyexplain human contour detection performance, but should alsoreproduce these systematic errors made by humans. Comparison betweenmisdetections of humans and mispredictions of the models on atrial-by-trial basis was used to evaluate different model dynamicsand association fields. This suggests that unidirectionalmultiplicatively coupled horizontal interactions are required inorder to explain human behavior. Furthermore, cortical magnificationfactors have to be taken into account and a fixed association fieldgeometry for all stimuli is preferable instead of using for eachcontour the association field employed for the generation of thiscontour.Dissertation356 122 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Inferring causal influences from expansive distortions between state space reconstructionsState space reconstructions of nonlinear dynamical system contain within their metric and topological properties information about the causal influences between different observables. The expansive distortions among different observables not only reflect the directed coupling strengths, but also the dependency of effective influences on the systems temporally varying state. Estimation of expansions from pairs of time series is straightforward, either directly from intra neighborhood relations or the mapping between reconstructions. Two approaches to compute expansive distortions are demonstrated using analytical and numerical analysis in a range of complex dynamical systems. The biggest challenge for the inference of causal influences is reached in synchronising systems or system perturbed by large amounts of noise. Remarkably, expansive distortions no only give in sight into just the interaction scheme, but provide a time-dependent measure for these interaction. These new methods offer a potential tool to gain insight into interactions of (nonlinear) dynamical system for a wide range of disciplines.Dissertation206 182