7,954 research outputs found

    Medical imaging analysis with artificial neural networks

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    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

    Linking landscape characteristics, streamwater acidity and brown trout (Salmo trutta) distributions in a boreal stream network

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    Perturbations of stream ecosystems are often mediated by the terrestrial watershed, making the understanding of linkages between watersheds and streams essential. In this thesis I explore the connections between landscape characteristics, streamwater acidity and brown trout (Salmo trutta) distributions in Krycklan, a 67 km2 boreal stream network in northern Sweden. The study focuses on hydrochemical changes during the snowmelt-driven spring flood, a period of episodic acidity which is thought to place a restraint on acid-sensitive biota such as brown trout. pH ranged from 4.5-7.0 at different stream sites during winter baseflow, and declined by 0-2 pH units during spring flood. The magnitude of the pH drop at a given site was in large part controlled by changes in acid neutralizing capacity (ANC) and in natural organic acids associated with dissolved organic carbon (DOC). pH, ANC and DOC were all correlated with landscape characteristics such as proportion of peat wetlands, and stream hydrochemical response during spring flood could be explained by altered hydrological flowpaths through the catchment. The impact of acidity on brown trout distributions within the stream network was evaluated and compared to the apparent influence of other site and catchment-scale environmental factors. In situ bioassays demonstrated a strong relationship between spring flood pH and juvenile brown trout mortality, with a toxicity threshold at pH 4.8-5.4. In field surveys brown trout were not found at any sites which had pH <5.0 during spring flood, and were rare at sites which had pH <5.5 during spring flood, suggesting limitation by acidity for some streams. However, over the whole of the Krycklan stream network brown trout were more consistently associated with alluvial sediment deposits than with high pH or low inorganic aluminum concentrations. Acidity thus apparently influences trout distributions by setting a maximum potential distribution; within that potential distribution, actual dispersal is influenced by other factors, notably presence of physical substrate suitable for feeding and spawning habitat. Fulfilling chemical thresholds is therefore necessary but not sufficient for sustaining brown trout populations. In the context of environmental monitoring or stream restoration, consideration of physical habitat together with chemical conditions is advised

    The Impact Of Spike-Frequency Adaptation On Balanced Network Dynamics

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    A dynamic balance between strong excitatory and inhibitory neuronal inputs is hypothesized to play a pivotal role in information processing in the brain. While there is evidence of the existence of a balanced operating regime in several cortical areas and idealized neuronal network models, it is important for the theory of balanced networks to be reconciled with more physiological neuronal modeling assumptions. In this work, we examine the impact of spike-frequency adaptation, observed widely across neurons in the brain, on balanced dynamics. We incorporate adaptation into binary and integrate-and-fire neuronal network models, analyzing the theoretical effect of adaptation in the large network limit and performing an extensive numerical investigation of the model adaptation parameter space. Our analysis demonstrates that balance is well preserved for moderate adaptation strength even if the entire network exhibits adaptation. In the common physiological case in which only excitatory neurons undergo adaptation, we show that the balanced operating regime in fact widens relative to the non-adaptive case. We hypothesize that spike-frequency adaptation may have been selected through evolution to robustly facilitate balanced dynamics across diverse cognitive operating states
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