Ernst, Udo Alexander
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Ernst, Udo Alexander
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Ernst, Udo Alexander
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Ernst, Udo A.
Ernst, Udo
GND
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Item type:Publication, Contour Integration : Attentional Effects in a Psychophysics Task and Feature Interactions in a Computational Model(2017-12-15); ; ; In order to achieve object recognition and image segmentation, the visual system is tasked with combining colinear and cocircular edge configurations into coherent percepts. This process is called Contour integration (CI). CI is believed to be a fundamental visual process, psychophysical experiments have shown humans to be remarkably good at integrating contours even when parts of the contours are occluded, or when a contour does not follow a smooth path. Electrophysiological studies have characterized the neural substrates of contour integration. Based on this information, modelling studies have produced algorithms to explain the functioning of putative mechanisms which give rise to CI. In this thesis, two case studies on contour integration are presented. In the first, psychophysical methods were employed to further characterize humans ability to detect contours under conditions of ambiguity. In particular, this study introduced a novel method in order to determine whether humans remarkable efficiency in detecting contours carries over to dynamic scenes. This is an important question given that scenes in nature are highly dynamic, and up to this point, most CI studies have characterized this process in static scenes. It has often been assumed that CI is a stimulus driven process which leads to pop-out percepts. Results from this study challenge these views. They indicate that humans ability to detect contours deteriorate drastically when shown extended presentations of dynamic stimuli. Furthermore, a set of sub-experiments indicates that top-down processes may play an important role in supporting contour integration under conditions of ambiguity. In the second case study, a computational model of contour integration was developed in order to account for new psychophysical findings, and further understand the mechanisms underlying these observations. Through a number of psychophysical studies, spatial frequency has been shown to be an important feature on which contours can defined and detected, and which can interact with the process of integrating oriented elements. Thus, a modulation component was added to a structurally simple model of contour integration in order to reproduce these findings. The modulation was based on the assumption that interactions of feature detectors are stronger if their preferred spatial frequencies are similar, rather than dissimilar. Extensive numerical simulations were carried out in order to understand the mechanisms leading to the mentioned psychophysical observations, and to reproduce said psychophysical results. This thesis presents contributions to the field of contour integration in two areas. In psychophysics, not only do the results from the experiments reported provide support for the emerging idea that CI may be supported by top-down process, but a significant methodological contribution was also made. A new technique to study CI was introduced. This will allow future research to characterize contour integration under new conditions. In the modeling field, a gap was bridged. To the knowledge of the author, the model presented in this thesis is the first to account for the geometrical characteristics of stimuli and the spatial frequency component of elements in the stimuli.doctoral thesis293 149 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Functional Role of Critical Dynamics in Flexible Visual Information Processing(2019-08-05); ; ; Recent experimental and theoretical work has established the hypothesis that cortical neurons operate close to a critical state which signifies a phase transition from chaotic to ordered dynamics. Critical dynamics are suggested to optimize several aspects of neuronal information processing. However, although signatures of critical dynamics have been demonstrated in recordings of spontaneously active cortical neurons, little is known about how these dynamics are affected by task-dependent changes in neuronal activity when the cortex is engaged in stimulus processing. In fact, some in vivo investigations of the awake and active cortex report either an absence of signatures of criticality or relatively weak ones. In addition, the functional role of criticality in optimizing computation is often reported in abstract theoretical studies, adopting minimalistic models with homogeneous topology and slowly-driven networks. Consequently, there is a lack of concrete links between information theoretical benefits of the critical state and neuronal networks performing a behaviourally relevant task. In this thesis we explore such concrete links by focusing on the visual system, which needs to meet major computational challenges on a daily basis. Among others, the visual system is responsible for the rapid integration of relevant information from a large number of single channels, and in a flexible manner depending on the behavioral and environmental contexts. We postulate that critical neuronal dynamics in the form of cascades of activity spanning large populations of neurons may support such quick and complex computations. Specifically, we consider two notable examples of well-known phenomena in visual information processing: First the enhancement of object discriminability under selective attention, and second, a feature integration and figure-ground segregation scenario. In the first example, we model the top-down modulation of the activity of visuocortical neurons in order to selectively improve the processing of an attended region in a visual scene. In the second example, we model how neuronal activity may be modulated in a bottom-up fashion by the properties of the visual stimulus itself, which makes it possible to perceive different shapes and objects. We find in both scenarios that the task performance may be improved by employing critical networks. In addition, we suggest that the specific task- or stimulus-dependent modulations of information processing may be optimally supported by the tuning of relevant local neuronal networks towards or away from the critical point. Thus, the relevance of this dissertation is summarized by the following points: We formally extend the existing models of criticality to inhomogeneous systems subject to a strong external drive. We present concrete functional benefits for networks operating near the critical point in well-known experimental paradigms. Importantly, we find emergent critical dynamics only in the parts of the network which are processing the behaviourally relevant information. We suggest that the implied locality of critical dynamics in space and time may help explain why some studies report no signatures of criticality in the active cortex.doctoral thesis339 192 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effective influences in neuronal networks : attentional modulation of effective influences underlying flexible processing and how to measure them(2018-04-20); ; ; Selective routing of information between brain areas is a key prerequisite for flexible adaptive behaviour. It allows to focus on relevant information and to ignore potentially distracting influences. Selective attention is a psychological process which controls this preferential processing of relevant information. The neuronal network structures and dynamics, and the attentional mechanisms by which this routing is enabled are not fully clarified. Based on previous experimental findings and theories, a network model is proposed which reproduces a range of results from the attention literature. It depends on shifting of phase relations between oscillating neuronal populations to modulate the effective influence of synapses. This network model might serve as a generic routing motif throughout the brain. The attentional modifications of activity in this network are investigated experimentally and found to employ two distinct channels to influence processing: facilitation of relevant information and independent suppression of distracting information. These findings are in agreement with the model and previously unreported on the level of neuronal populations. Furthermore, effective influence in dynamical systems is investigated more closely. Due to a lack of a theoretical underpinning for measurements of influence in non-linear dynamical systems such as neuronal networks, often unsuited measures are used for experimental data that can lead to erroneous conclusions. Based on a central theorem in dynamical systems, a novel theory of effective influence is developed. Measures derived from this theory are demonstrated to capture the time dependent effective influence and the asymmetry of influences in model systems and experimental data. This new theory holds the potential to uncover previously concealed interactions in generic non-linear systems studied in a range of disciplines, such as neuroscience, ecology, economy and climatology.doctoral thesis564 231 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Spontaneous synchronization in recurrent neural networks: From mathematical analysis to flexible information processing in spiking networks(2023-04-20); ; ; The brain is a highly complex distributed system that not only continuously performs demanding computational tasks but also flexibly adapts its processing to changing requirements on short time scales. A prime example of this flexibility is provided by selective visual attention. Neurons in the visual cortex have been shown to respond to simultaneous presentation of multiple stimuli in their receptive field as if only the stimulus receiving attention was present. This constitutes an attention mediated change of the functional configuration of information processing resulting in selective routing of the behaviorally relevant signals. It is not fully understood which neural mechanisms enable flexible information processing in the brain. This thesis uses theoretical analysis, model building and experimental investigations to generate novel insights into the key aspects coordination, configuration and control of flexible information processing. On a fundamental level, neural activity consists of spatio-temporal spike patterns which can contain transient bursts of synchronous activity of an assembly of neurons in form of spike avalanches. Theoretical studies suggest that a critical dynamics exhibiting power law distributed avalanche sizes maximizes computational capabilities, but usually assume homogeneously coupled populations in the large network size limit. Neural populations in the brain are instead finite, highly structured and strongly driven. To help bridge this gap, we develop a theory of spike pattern formation for finite and arbitrarily coupled networks of spiking neurons resulting in closed form avalanche assembly distributions. This closed form contains the networks graph Laplacian and thus provides explicit insights into how the network structure coordinates spontaneously synchronized activity.doctoral thesis443 381 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Reverberating Brain Bits: Scrutinizing Information Routing by Synchronization(2021-12-21); ; ; At any given moment, the brain is subjected to a barrage of sensory information from the environment around us. Selective attention allows to focus on the behaviourally-relevant subset within this overwhelming information stream, depending on the task at hand, and ignore the rest. Attended signals are routed through to deeper parts of the brain, getting processed and reaching awareness, whereas the routing of non-attended signals is suppressed. Despite extensive evidence for attention-dependent changes in neural activity, the mechanism behind such flexible routing of information is not well understood. One possibility is proposed by the routing by synchrony (RBS) mechanism -- wherein modulation of effective connectivity between neural populations is established by gamma-rhythmic synchronization and coherence of their neural activities. Neural populations receiving attended signals can align their gamma-rhythmic activity to that of the sending populations, such that incoming spikes would arrive when the receiving population is most excitable, enhancing signal transfer. Conversely, non-attended signals arrive unaligned to the receiver's rhythm, reducing their transfer. Experimental studies have already provided correlative evidence that gamma-rhythmic coherence between populations processing visual stimuli is modulated by attention. However, it remains to be shown whether the observed synchronization and coherence phenomena are responsible for flexible information routing causally. Here, we investigate RBS using two complementary approaches, aiming to establish a causal link between physiological phenomena and the neural mechanisms responsible for selective information processing. In the first project, we make a prediction, based on previous experimental findings and the theory behind the RBS mechanism -- information encoded within neural activity should itself be conveyed through gamma-rhythmic packages. To investigate this prediction, we perform an intricate analysis on neural recordings collected from monkeys performing a visual selective attention task, which utilized visual stimuli allowing to assess their information content within neural activity. New methods are developed, allowing to compute how stimulus information content depends on the gamma phase and amplitude of neural activity. The results are in agreement with the prediction, showing that stimulus information is indeed conveyed via gamma-rhythmic packages, in support of the RBS mechanism. In the second project, via a modeling approach, we investigate the consequences of direct microstimulation of neural circuits involved in selective attention. Using stimulation pulses in order to control gamma-rhythmic coherence between neural populations would allow to directly test the efficacy of the RBS mechanism and potentially control information routing itself. We propose a method that relies on the inherent oscillatory dynamics of the system -- applying minimal stimulation to nudge the natural activity of the network into a desired state. A closed-loop stimulation paradigm is developed, based on the phase-response characteristics of a biophysically realistic network performing selective routing of information. This allows us measure the contamination of stimulus information content by artificial pulses. Within the scope of our model, we demonstrate that precisely timed perturbations can be used to artificially induce the effect of attention by selectively routing visual signals to higher cortical areas and pinpoint caveats and limitations of utilizing this approach in vivo.doctoral thesis305 153