Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item The Curved Openspace Algorithm and Neuromorphic Mechanisms for Sonar-Based Obstacle Avoidance(2021) Wen, Chenxi; Horiuchi, Timothy TKH; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Bats are known for their ability to pursue a goal while avoiding obstacles in a cluttered environment using ultrasonic echolocation. This dissertation explores two neuromorphic mechanisms involved in such a task: a conductance-based neuron circuit for azimuthal echolocation whose dynamic range can be expanded by power-law compression, and a sonar-based obstacle avoidance algorithm with its spike-latency model implementation. Bats and other mammals use interaural level differences (ILD) to estimate the direction of high-frequency sounds. To compute the ILD of a sound, independent of overall loudness, excitatory and inhibitory synaptic conductances (encoding the left and right amplitudes) are hypothesized to compete in the neurons of the lateral superior olive. Interestingly, this neural model can also accept power-law compressed amplitudes that can allow a much larger range of input signal levels, a common limitation in neural coding. This dissertation demonstrates the use of square-root and cube-root compression with a neuromorphic VLSI neuron to expand the range of distances over which ILD can be used to estimate echo direction in a sonar system based on echolocating bats. However, many questions remain regarding how to achieve the rapid control of a sonar-guided vehicle to pursue a goal while avoiding obstacles. Taking into account the limited field-of-view of practical sonar systems and vehicle kinematics, we propose an obstacle avoidance algorithm that maps the 2-D sensory space into a 1-D motor space and evaluates motor actions while combining obstacles and goal information. A winner-take-all (WTA) mechanism is used to select the final steering action. To avoid unnecessary scanning of the environment, an attentional system is proposed to control the directions of sonar pings for efficient, task-driven, sensory data collection. A mobile robot driven by the proposed algorithm was capable of navigating through a cluttered environment using a realistic sonar system. The algorithm was tested on a mobile robot, and it is implemented on a neural model using spike-timing representations, a spike-latency memory, and a “race-to-first-spike” WTA circuit. This dissertation also proposed a CMOS floating-gate circuit for artificial neural network synapse memories that can achieve a fixed rate of weight increase (adding vectors) or proportional decay (normalization) on the synaptic weight.Item A MULTIPLE REPRESENTATIONS MODEL OF THE HUMAN MIRROR NEURON SYSTEM FOR LEARNED ACTION IMITATION(2015) Oh, Hyuk; Gentili, Rodolphe J; Neuroscience and Cognitive Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The human mirror neuron system (MNS) is a fundamental sensorimotor system that plays a critical role in action observation and imitation. Despite a large body of experimental and theoretical MNS studies, the visuospatial transformation between the observed and the imitated actions has received very limited attention. Therefore, this work proposes a neurobiologically plausible MNS model, which examines the dynamics between the fronto-parietal mirror system and the parietal visuospatial transformation system during action observation and imitation. The fronto-parietal network is composed of the inferior frontal gyrus (IFG) and the inferior parietal lobule (IPL), which are postulated to generate the neural commands and the predictions for its sensorimotor consequences, respectively. The parietal regions identified as the superior parietal lobule (SPL) and the intraparietal sulcus (IPS) are postulated to encode the visuospatial transformation for enabling view-independent representations of the observed action. The middle temporal region is postulated to provide the view-dependent representations such as direction and velocity of the observed action. In this study, the SPL/IPS, IFG, and IPL are modeled with artificial neural networks to simulate the neural mechanisms underlying action imitation. The results reveal that this neural model can replicate relevant behavioral and neurophysiological findings obtained from previous action imitation studies. Specifically, the imitator can replicate the observed actions independently of the spatial relationships with the demonstrator while generating similar synthetic functional magnetic resonance imaging blood oxygenation level-dependent responses in the IFG for both action observation and execution. Moreover, the SPL/IPS can provide view-independent visual representations through mental transformation for which the response time monotonically increases as the rotation angle augments. Furthermore, the simulated neural activities reveal the emergence of both view-independent and view-dependent neural populations in the IFG. As a whole, this work suggests computational mechanisms by which visuospatial transformation processes would subserve the MNS for action observation and imitation independently of the differences in anthropometry, distance, and viewpoint between the demonstrator and the imitator.