1,720,952 research outputs found
Analysing the performance of KHFAC nerve block stimulation parameters: Developing design considerations for blocking the pudendal nerve using a new gate-dependent block determination model
No full textA major cause for voiding dysfunction is the inability to relax the urethral sphincter. KiloHertz Frequency Alternating Current (KHFAC) stimulation can block signals that are travelling through the body; applying this type of stimulation at the pudendal nerve could inhibit the pulses that lead to contraction of the urethral sphincter, and restore voiding ability. In order to design a successful KHFAC block therapy for the pudendal nerve, it is necessary to understand what impact different stimulation parameters have on efficacy, safety and power-efficiency. This thesis will therefore test earlier researched KHFAC stimulation parameters against a new quality measure, study the impact of new waveform alterations, and study how bipolar electrode design can improve KHFAC therapy. By utilizing the theory behind the mechanism of the KHFAC nerve block, a new block-determination model was developed that is over thirty times faster than the classic model. The McIntyre-Richardson-Grill model was chosen as the implementation of the axon model, and the bipolar electrode was modelled as an electric dipole. The simulation experiments revealed that the charge per phase of the KHFAC signal at block threshold could be reduced, without increasing the amplitude of the signal, by introducing interphase delays to the waveforms and by creating asymmetric charge-balanced waveforms. Triangular waveforms were shown to also require less charge per phase than a regular square wave to block, albeit with a higher amplitude. A correctly aligned bipolar electrode set-up with an interpolar distance that was about the same as the electrode-to-axon distance was shown to result in reduced block thresholds. Overall, this thesis has shown how stimulation parameters can be chosen to develop an effective KHFAC block therapy for the pudendal nerve.REValUEElectrical Engineerin
Parkisonian Resting Tremor: Source and Interaction with Movement
No full textBiologically inspired neural networks are a promising approach to understand the causes and improve the treatments of brain damage. Parkinson's disease is a progressive nervous system disorder that affects mainly movements, speech and cognitive problems. It symptoms cannot be cured, though medications can significantly improve the condition. Among the symptoms, tremor is the only one which remains unaffected by medications and is only responsive to deep-brain stimulation. A simplified, cortico-thalamo-cerebellar model will be simulated with spiking neural networks to evaluate the disease effects under dopamine depletion and connectivity weight changes. Confirming previous findings, striatal dopamine depletion was not found to cause tremor, nor its injection to affect tremor severity. The model showed evidence that parkinsonian weight changes in the pallidal inner feedback loop (GPi-GPe) are responsible of creating a suitable environment for the PD tremor oscillations to rise in the thalamus. Furthermore, both the GPi and the GPe present enhanced maximal activity coherent with muscular co-contraction onsets showing evidence of abnormal basal ganglia firing during re-emergent tremor. These findings may connect abnormal basal ganglia activity to the main parkinsonian motor impairments and may help explaining the beneficial effects of deep-brain stimulation on tremor severity
Scalable GPU Acceleration for Complex Brain Simulations
No full textComplex mathematical models are used in computational neuroscience to stimulate brain activity to understand the biological processes involved. The simulation of such models is computationally costly, and thus highperformancecomputing systems are selected as a potential solution to increase performance.This thesis aims to implement a new versatile, multi-GPU eHH simulator (mgpuHH), explore its performance and make general observations on performance scalability over different modeling and cluster configuration properties. This work offers a multinode multi-GPU solution that offers excellentscalability performance due to how the simulator is constructed, with the use of OpenMPI and CUDA. The simulator is configured with JSON configuration files, containing the neural descriptions and simulatorspecific settings. Consequently, enabling a userfriendly environment, for the neuroscientists, without the need of recompiling or understanding the source code. The gap junction calculations are identified as the critical function bottlenecking performance of the simulator. Therefore, an algorithm tailored to utilize GPU performance is implemented to decrease wallclock time for these specific calculations. For internodecommunication, OpenMPI can be configured in two ways. Eiter share all possible compartments potentials with every node in the network or only share the compartments potentials to nodes that need them. These methods rely internally on MPI Allgather and Alltoallv respectively. When available, GPUDirect, NVlink, and RDMA are supported. The implementation hides communication overhead, when possible, by concurrently executable compute kernels. A neuron model from the Inferior Olivary Nucleus is selected for benchmarking. Reported results go up to 32 Nodes with a total of 64 GPU cards. The design shows linear weak and strong scaling within the experimental setups for intranode and internode scalability. With this simulator, networks over 10 million cells become available to model on largescale GPU clusters, setting a new standard for eHH simulations. Comparisons against related work on CPU and FPGAs have been conducted, a 100x speedup is achieved versus a single cpu threaded solution. Furthermore, a 2x speedup is achieved over an FPGA solution (flexHH) and 10 fold over a multithreaded CPU (GenEHH, with 128 threads) solution, both reported speedups are for a fully connected network with 7000 IO cells.Computer Engineerin
Balancing Long-term Plasticity Mechanisms at the Purkinje Cell in the Olivocerebellar System
No full textThe olivocerebellar system plays a crucial role in control of movements of the human body in terms of coordination, precision and timing. Long-term plasticity is directly linked to motor learning and control. In this research, we developed a phenomenological model of the olivocerebellar system with balancing of long-term potentiation (LTP) and long-term depression (LTD) at the parallel fiber-Purkinje cell (PF-PC) synapse. By ranging the PF input over frequencies, we found that PCs can select frequencies in a highly non-linear manner. There is a sharp contrast in synaptic weight change between neighbouring frequencies, which is caused by the temporal spiking property of the inferior olive (IO) cell. This research found a novel signal processing capability of the PC.Mechanical Engineering | BioMechanical Desig
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Encoding of Correlated Temporal Information in a Model Cerebellar Loop with Olivary Oscillators and Long-Term Plasticity
No full textThe olivocerebellar system plays a central role in motor learning, crucially contributing to the coordination, precision and accurate timing of movements. The system is formed by Purkinje cells (PC), the Deep Cerebellar Nucleus (DCN) and Inferior Olive (IO). Its input activate the PC which produces simple (SS) and complex spikes (CS). The latter induced by the IO. The oscillatory nature of the production of CS seems to play a role in motor control and motor timing. In addition, CS modulate the parallel fiber-Purkinje cell (PF-PC) synaptic plasticity. IO synchrony can help the system learn timing with the PF input as the timing context. Furthermore, the level of synchronization of the coupled IO cells could determine the function of complex spikes, implying that the olivocerebellum is capable of switching between modes of learning by changing the level of synchronization. In this paper we introduced a novel computational model to analyze the role of coupling of the IO and long-term plasticity at the PF-PC synapse in the response of the olivocerebellar loop. It is a resonant system formed by a detailed IO model and Integrate-and-Fire PC and DCN models, with physiologically observed firing frequencies. The IO cells are modeled as coupled oscillators and plasticity is incorporated through a timing dependent specialization of Hebbian learning (Spike-Timing Dependent Plasticity or STDP). Two different simulations are performed both for the coupled and uncoupled scenarios. The STDP is used only in the second type of simulation. Both types of simulations use the same noisy input, which is applied twice during the second type of simulation. The second half of the latter is interpreted as the response of a trained loop. Results show that in the presence of coupling the correlation of the firing rates distribution decreases. This indicates that for the coupled scenario PCs are separating the patterns, while for the uncoupled scenarios the noise is encoded more robustly. Furthermore, a drop in the noise current (inhibiting the PC) leads to an IO spike at about 100 milliseconds later. After training, however, the loop recognizes a drop in the noise and depresses the synapses avoiding an increase in the firing rate of the PC. This effect is more noticeable in the coupled scenario. In conclusion, the model shows that plasticity can lead to learning, and that this process is more efficient for a coupled system.Biomedical Engineerin
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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