ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Neurosciences
Description
Multisensory integration is the process by which information from different sensory modalities is integrated by the nervous system. This process is important not only from a basic science perspective but also for translational reasons, e.g., for the development of closed-loop neural prosthetic systems. A mixed virtual reality platform was developed to study the neural mechanisms of multisensory integration for the upper limb during motor planning. The platform allows for selection of different arms and manipulation of the locations of physical and virtual target cues in the environment. The system was tested with two non-human primates (NHP) trained to reach to multiple virtual targets. Arm kinematic data as well as neural spiking data from primary motor (M1) and dorsal premotor cortex (PMd) were collected. The task involved manipulating visual information about initial arm position by rendering the virtual avatar arm in either its actual position (veridical (V) condition) or in a different shifted (e.g., small vs large shifts) position (perturbed (P) condition) prior to movement. Tactile feedback was modulated in blocks by placing or removing the physical start cue on the table (tactile (T), and no-tactile (NT) conditions, respectively). Behaviorally, errors in initial movement direction were larger when the physical start cue was absent. Slightly larger directional errors were found in the P condition compared to the V condition for some movement directions. Both effects were consistent with the idea that erroneous or reduced information about initial hand location led to movement direction-dependent reach planning errors. Neural correlates of these behavioral effects were probed using population decoding techniques. For small shifts in the visual position of the arm, no differences in decoding accuracy between the T and NT conditions were observed in either M1 or PMd. However, for larger visual shifts, decoding accuracy decreased in the NT condition, but only in PMd. Thus, activity in PMd, but not M1, may reflect the uncertainty in reach planning that results when sensory cues regarding initial hand position are erroneous or absent.
ContributorsPhataraphruk, Preyaporn Kris (Author) / Buneo, Christopher A (Thesis advisor) / Zhou, Yi (Committee member) / Helms Tillery, Steve (Committee member) / Greger, Bradley (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2023
Description
Neuron models that behave like their biological counterparts are essential for computational neuroscience.Reduced neuron models, which abstract away biological mechanisms in the interest of speed and interpretability, have received much attention due to their utility in large scale simulations of the brain, but little care has been taken to ensure that these models exhibit behaviors that closely resemble real neurons.
In order to improve the verisimilitude of these reduced neuron models, I developed an optimizer that uses genetic algorithms to align model behaviors with those observed in experiments.
I verified that this optimizer was able to recover model parameters given only observed physiological data; however, I also found that reduced models nonetheless had limited ability to reproduce all observed behaviors, and that this varied by cell type and desired behavior.
These challenges can partly be surmounted by carefully designing the set of physiological features that guide the optimization. In summary, we found evidence that reduced neuron model optimization had the potential to produce reduced neuron models for only a limited range of neuron types.
In order to improve the verisimilitude of these reduced neuron models, I developed an optimizer that uses genetic algorithms to align model behaviors with those observed in experiments.
I verified that this optimizer was able to recover model parameters given only observed physiological data; however, I also found that reduced models nonetheless had limited ability to reproduce all observed behaviors, and that this varied by cell type and desired behavior.
These challenges can partly be surmounted by carefully designing the set of physiological features that guide the optimization. In summary, we found evidence that reduced neuron model optimization had the potential to produce reduced neuron models for only a limited range of neuron types.
ContributorsJarvis, Russell Jarrod (Author) / Crook, Sharon M (Thesis advisor) / Gerkin, Richard C (Thesis advisor) / Zhou, Yi (Committee member) / Abbas, James J (Committee member) / Arizona State University (Publisher)
Created2020
Description
It is increasingly common to see machine learning techniques applied in conjunction with computational modeling for data-driven research in neuroscience. Such applications include using machine learning for model development, particularly for optimization of parameters based on electrophysiological constraints. Alternatively, machine learning can be used to validate and enhance techniques for experimental data analysis or to analyze model simulation data in large-scale modeling studies, which is the approach I apply here. I use simulations of biophysically-realistic cortical neuron models to supplement a common feature-based technique for analysis of electrophysiological signals. I leverage these simulated electrophysiological signals to perform feature selection that provides an improved method for neuron-type classification. Additionally, I validate an unsupervised approach that extends this improved feature selection to discover signatures associated with neuron morphologies - performing in vivo histology in effect. The result is a simulation-based discovery of the underlying synaptic conditions responsible for patterns of extracellular signatures that can be applied to understand both simulation and experimental data. I also use unsupervised learning techniques to identify common channel mechanisms underlying electrophysiological behaviors of cortical neuron models. This work relies on an open-source database containing a large number of computational models for cortical neurons. I perform a quantitative data-driven analysis of these previously published ion channel and neuron models that uses information shared across models as opposed to information limited to individual models. The result is simulation-based discovery of model sub-types at two spatial scales which map functional relationships between activation/inactivation properties of channel family model sub-types to electrophysiological properties of cortical neuron model sub-types. Further, the combination of unsupervised learning techniques and parameter visualizations serve to integrate characterizations of model electrophysiological behavior across scales.
ContributorsHaynes, Reuben (Author) / Crook, Sharon M (Thesis advisor) / Gerkin, Richard C (Committee member) / Zhou, Yi (Committee member) / Baer, Steven (Committee member) / Armbruster, Hans D (Committee member) / Arizona State University (Publisher)
Created2020
Description
This study focuses on the properties of binaural beats (BBs) compared to Monaural beats (MBs) and their steady-state response at the level of the Superior Olivary Complex (SOC). An auditory nerve stimulator was used to simulate the response of the SOC. The simulator was fed either BBs or MBs stimuli to compare the SOC response. This was done for different frequencies at twenty, forty, and sixty hertz for comparison of the SOC response envelopes. A correlation between the SOC response envelopes for both types of beats and the waveform resulting from adding two tones together was completed. The highest correlation for BBs was found to be forty hertz and for MBs it was sixty hertz. A Fast Fourier Transform (FFT) was also completed on the stimulus envelope and the SOC response envelopes. The FFT was able to show that within the BBs presentation the envelopes of the original stimuli showed no difference frequency. However, the difference frequency was present in the binaural SOC response envelope. For the MBs, the difference frequency was present within the stimulus and the monaural SOC response envelope.
ContributorsCrawford, Taylor Janay (Author) / Brewer, Gene (Thesis advisor) / Zhou, Yi (Committee member) / Azuma, Tamiko (Committee member) / Arizona State University (Publisher)
Created2021
Description
Diffusion Tensor Imaging may be used to understand brain differences within PD. Within the last couple of decades there has been an explosion of learning and development in neuroimaging techniques. Today, it is possible to monitor and track where a brain is needing blood during a specific task without much delay such as when using functional Magnetic Resonance Imaging (fMRI). It is also possible to track and visualize where and at which orientation water molecules in the brain are moving like in Diffusion Tensor Imaging (DTI). Data on certain diseases such as Parkinson’s Disease (PD) has grown considerably, and it is now known that people with PD can be assessed with cognitive tests in combination with neuroimaging to diagnose whether people with PD have cognitive decline in addition to any motor ability decline. The Montreal Cognitive Assessment (MoCA), Modified Semantic Fluency Test (MSF) and Mini-Mental State Exam (MMSE) are the primary tools and are often combined with fMRI or DTI for diagnosing if people with PD also have a mild cognitive impairment (MCI). The current thesis explored a group of cohort of PD patients and classified based on their MoCA, MSF, and Lexical Fluency (LF) scores. The results indicate specific brain differences in whether PD patients were low or high scorers on LF and MoCA scores. The current study’s findings adds to the existing literature that DTI may be more sensitive in detecting differences based on clinical scores.
ContributorsAndrade, Eric (Author) / Oforoi, Edward (Thesis advisor) / Zhou, Yi (Committee member) / Liss, Julie (Committee member) / Arizona State University (Publisher)
Created2022
Description
The ability to detect and correct errors during and after speech production is essential for maintaining accuracy and avoiding disruption in communication. Thus, it is
crucial to understand the basic mechanisms underlying how the speech-motor system
evaluates different errors and correspondingly corrects them. This study aims to explore
the impact of three different features of errors, introduced by formant perturbations, on
corrective and adaptive responses: (1) magnitude of errors, (2) direction of errors, and (3)
extent of exposure to errors. Participants were asked to produce the vowel /ε/ in the
context of consonant-vowel-consonant words. Participant-specific formant perturbations
were applied for three magnitudes of 0.5, 1, 1.5 along the /ε-æ/ line in two directions of
simultaneous F1-F2 shift (i.e., shift in the ε-æ direction) and shift to outside the vowel
space. Perturbations were applied randomly in a compensation paradigm, so each
perturbed trial was preceded and succeeded by several unperturbed trials. It was observed
that (1) corrective and adaptive responses were larger for larger magnitude errors, (2)
corrective and adaptive responses were larger for errors in the /ε-æ/ direction, (3)
corrective and adaptive responses were generally in the /ε-ɪ/ direction regardless of
perturbation direction and magnitude, (4) corrective responses were larger for
perturbations in the earlier trials of the experiment.
ContributorsSreedhar, Anuradha Jyothi (Author) / Daliri, Ayoub (Thesis advisor) / Rogalsky, Corianne (Committee member) / Zhou, Yi (Committee member) / Arizona State University (Publisher)
Created2024