Global ETD Search

21	Modelling Low Dimensional Neural Activity / Modellering av lågdimensionell neural aktivitet Wärnberg, Emil January 2016 (has links) A number of recent studies have shown that the dimensionality of the neural activity in the cortex is low. However, what network structures are capable of producing such activity is not theoretically well understood. In this thesis, I discuss a few possible solutions to this problem, and demonstrate that a network with a multidimensional attractor can give rise to such low dimensional activity. The network is created using the Neural Engineering Framework, and exhibits several biologically plausible features, including a log-normal distribution of the synaptic weights. / Ett antal nyligen publicerade studier has visat att dimensionaliten för neural aktivitet är låg. Dock är det inte klarlagt vilka nätverksstrukturer som kan uppbringa denna typ av aktivitet. I denna uppsats diskuterar jag möjliga lösningsförslag, och demonstrerar att ett nätverk med en flerdimensionell attraktor ger upphov till lågdimensionell aktivitet. Nätverket skapas med hjälp av the Neural Engineering Framework, och uppvisar ett flertal biologiskt trovärdiga egenskaper. I synnerhet är fördelningen av synapsvikter log-normalt fördelad. neural networks low dimensional neural activity neural engineering framework NEF nef artificial neural network ann dimensionality log-normal log normal distribution synaptic geometric connectivity Bioinformatics (Computational Biology) Bioinformatik (beräkningsbiologi)
22	Novel Carbon-Nanotube Based Neural Interface for Chronic Recording of Glossopharyngeal Nerve Activity Kostick, Nathan H. 01 June 2018 (has links) No description available. Biomedical Engineering
23	Direct Current Block of Peripheral Nerve: Electrode and Waveform Development Vrabec, Tina L. 27 January 2016 (has links) No description available. Biomedical Engineering Biomedical Research Chemical Engineering Engineering Electrical Engineering Neurosciences Nerve Block KHFAC Direct Current DC Neural Engineering electrode platinum black iridium oxide high capacitance electrode nerve damage
24	Reversible Nerve Conduction Block Using Low Frequency Alternating Currents Maria I. Muzquiz (9178664), Ivette M Muzquiz (9178658) 05 August 2020 (has links) This thesis describes a novel method to reversibly and safely block nerve conduction using a low frequency alternating current (LFAC) waveform at 1 Hz applied through a bipolar extrafascicular electrode. This work follows up on observations made on excised mammalian peripheral nerves and earthworm nerve cords. An<i> in-situ</i> electrophysiology setup was used to assess the LFAC<br>waveform on propagating action potentials (APs) within the cervical vagus nerve in anaesthetized Sprague-Dawley rats (n = 12). Two sets of bipolar cuff or hook electrodes were applied unilaterally to the cervical vagus nerve, which was crushed rostral to the electrodes to exclude reflex effects<br>on the animal. Pulse stimulation was applied to the rostral electrode, while the LFAC conditioning waveform was applied to the caudal electrode. The efferent volley, if unblocked, elicits acute bradycardia and hypotension. The degree of block of the vagal stimulation induced bradycardia<br>was used as a biomarker. Block was assessed by the ability to reduce the bradycardic drive by monitoring the heart rate (HR) and blood pressure (BP) during LFAC alone, LFAC with vagal stimulation, and vagal stimulation alone. LFAC applied via a hook electrode (n = 7) achieved 86.6 +/- 11% block at current levels 95 +/- 38 uAp (current to peak). When applied via a cuff electrode (n = 5) 85.3 +/- 4.60% block was achieved using current levels of 110 +/- 65 uAp. Furthermore, LFAC was explored on larger vagal afferent fibers in larger human sized nerve bundles projecting to effects mediated by a reflex. The effectiveness of LFAC was assessed in an <i>in-situ</i> electrophysiological setup on the left cervical vagus in anaesthetized domestic swine (n = 5). Two bipolar cuff electrodes were applied unilaterally to the cervical vagus nerve, which was crushed caudal to the electrodes to eliminate cardiac effects. A tripolar extrafascicular cuff electrode was placed most rostral on the nerve for recording of propagating APs induced by<br>electrical stimulation and blocked via the LFAC waveform.<br>Standard pulse stimulation was applied to the left cervical vagus to induce the Hering-Breuer reflex. If unblocked, the activation of the Hering-Breuer reflex would cause breathing to slow down and potentially cease. Block was quantified by the ability to reduce the effect of the Hering-Breuer<br>reflex by monitoring the breathing rate during LFAC alone, LFAC and vagal stimulation, and vagal stimulation alone. LFAC achieved 87.2 +/- 8.8% (n = 5) block at current levels of 0.8 +/- 0.3 mAp. Compound nerve action potentials (CNAP) were monitored directly. They show changes<br>in nerve activity during LFAC, which manifests itself as the slowing and amplitude reduction of components of the CNAPs. Since the waveform is balanced, all forward reactions are reversed, leading to a blocking method that is similar in nature to DC block without the potential issues of<br>toxic byproduct production. These results suggest that LFAC can achieve a high degree of nerve block in both small and large nerve bundles, resulting in the change in behavior of a biomarker, <i>in-vivo </i>in the mammalian nervous system at low amplitudes of electrical stimulation that are within the water window of the electrode.<br> Biomedical Instrumentation Vagal Stimulation Cervical Vagus Nerve Nerve Block Conduction Block Reversible Biomarker Bradycardia Hering-Breuer Reflex Hypotension Heart Rate Breathing Rate Neural Engineering Neural Overactivity Electrode Sensitivity Function Action Potential Compound Neural Action Potential Electrode Bipolar Tripolar Low Frequency Nerve Block
25	On the Role of, and Intervention in, Oxygen-Conserving Reflexes in Sudden Unexpected Death in Epilepsy Ethan N Biggs (13199502) 04 August 2022 (has links) <p>Sudden unexpected death in epilepsy (SUDEP) is a fatal complication of epilepsy that kills 1̃2 of every 10,000 epileptic patients every year. SUDEP has proven difficult to study because it frequently occurs unobserved and cannot be predicted. What limited clinical data exists suggests that SUDEP occurs as a cardiorespiratory collapse immediately following a seizure. In this work, I explore how a group of autonomic reflexes termed collectively as “oxygen‐conserving reflexes (OCRs)” lead to sudden death when activated during seizures. I also demonstrate multiple physiological parallels between the OCR‐mediated deaths that I report and the clinical data on cases of human SUDEP. Additionally, I explore the neural pathway underlying OCRs, identify the carotid body as a potential target for intervention, and demonstrate the efficacy of electroceutical intervention in reducing the mortality risk of OCR activation during seizures. This work seeks to both offer a neural explanation for SUDEP as well as present a promising target and means for potential intervention.</p> Systems physiology Autonomic nervous system Central nervous system Peripheral nervous system Sensory systems Neurosciences not elsewhere classified Biomedical instrumentation Medical devices Neural engineering Analog electronics and interfaces Electronic instrumentation oxygen-conserving reflexes diving reflex mammalian diving reflex chemoreflex laryngeal chemoreflex epilepsy SUDEP sudden death SIDS carotid body carotid sinus carotid sinus nerve nerve stimulation peripheral nerve stimulation electrical nerve stimulation Autonomic nervous system (ANS) autonomic reflexes pathophysiology apnea seizure
26	EXPLORING GRAPH NEURAL NETWORKS FOR CLUSTERING AND CLASSIFICATION Fattah Muhammad Tahabi (14160375) 03 February 2023 (has links) <p><strong>Graph Neural Networks</strong> (GNNs) have become excessively popular and prominent deep learning techniques to analyze structural graph data for their ability to solve complex real-world problems. Because graphs provide an efficient approach to contriving abstract hypothetical concepts, modern research overcomes the limitations of classical graph theory, requiring prior knowledge of the graph structure before employing traditional algorithms. GNNs, an impressive framework for representation learning of graphs, have already produced many state-of-the-art techniques to solve node classification, link prediction, and graph classification tasks. GNNs can learn meaningful representations of graphs incorporating topological structure, node attributes, and neighborhood aggregation to solve supervised, semi-supervised, and unsupervised graph-based problems. In this study, the usefulness of GNNs has been analyzed primarily from two aspects - <strong>clustering and classification</strong>. We focus on these two techniques, as they are the most popular strategies in data mining to discern collected data and employ predictive analysis.</p> Biomechanical engineering Neural engineering Health promotion Preventative health care Applications in health Spatial data and applications Evolutionary computation Natural language processing Planning and decision making Data engineering and data science Data mining and knowledge discovery Graph, social and multimedia data Information retrieval and web search Knowledge and information management Context learning Deep learning Neural networks Semi- and unsupervised learning Data structures and algorithms Graph neural network Node classification Graph clustering Temporal graphs dynamic graphs NODE2VEC Graph Attention Mechanism Hunting BiLSTM model EHR data colorectal Cancer Cancers Cancer symptoms symptom Symptom cluster studies Coauthorship networks network analysis Word2vec Hierarchical Clustering method Dunn index semantic analysis text mining Natural Language Processing Tool UMLS identifiers umls Clinical Data Management

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