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 Statistical Models of Neural Computations and Network Interactions in High-Dimensional Neural Data(2023) Mukherjee, Shoutik; Babadi, Behtash; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recent advances in neural recording technologies, like high-density electrodes and two-photon calcium imaging, now enable the simultaneous acquisition of several hundred neurons over large patches of cortex. The availability of high volumes of simultaneously acquired neural activity presents exciting opportunities to study the network-level properties that support the neural code. This dissertation consists of two themes in analyzing network-level neural coding in large populations, particularly in the context of audition. Namely, we address modeling the instantaneous and directed interactions in large neuronal assemblies; and modeling neural computations in the mammalian auditory system.In the first part of this dissertation, an algorithm for adaptively modeling higher-order coordinated spiking as a discretized mark point process is proposed. Analyzing coordinated spiking involves a large number of possible simultaneous spiking events and covariates. We propose the adaptive Orthogonal Matching Pursuit (AdOMP) to tractably model dynamic higher-order coordination of ensemble spiking. Moreover, we generalize an elegant procedure for constructing confidence intervals for sparsity-regularized estimates to greedy algorithms and subsequently derive an inference framework for detecting facilitation or suppression of coordinated spiking. Application to simulated and experimentally recorded multi-electrode data recordings reveals significant gains over several existing benchmarks. The second part pertains to functional network analysis of large neuronal ensembles using OMP to impose sparsity constraints on models of neuronal responses. The efficacy of functional network analysis based on greedy model estimation is first demonstrated in two sets of two-photon calcium imaging data of mouse primary auditory cortex. The first dataset was collected during a tone discrimination task, where we additionally show that properties of the functional network structure encode information relevant to the animal’s task performance. The second dataset was collected from a cohort of young and aging mice during passive presentations of pure-tones in noise to study aging-related network changes in A1. The constituency of neurons engaged in functional networks changed by age; we characterized these changes and their correspondence to differences in functional network structure. We next demonstrated the efficacy of greedy estimation in functional network analysis in application to electrophysiological spiking recordings across multiple areas of songbird auditory cortex, and present initial findings on interareal network structure differences between responses to tutor songs and non-tutor songs that suggest the learning-related effects on functional networks. The third part of this dissertation concerns neural system identification. Neu- rons in ferret primary auditory cortex are known to exhibit stereotypical spectrotem- poral specificity in their responses. However, spectrotemporal receptive fields (STRF) measured in non-primary areas can be intricate, reflecting mixed spectrotemporal selectivity, and hence be challenging to interpret. We propose a point process model of spiking responses of neurons in PEG, a secondary auditory area, where neurons’ spiking rates are modulated by a high-dimensional biologically inspired stimulus rep- resentation. The proposed method is shown to accurately model a neuron’s response to speech and artificial stimuli, and offers the interpretation of complex STRFs as the sparse combination of higher-dimensional features. Moreover, comparative analyses between PEG and A1 neurons suggest the role of such an hierarchical model is to facilitate encoding natural stimuli.The fourth part of this dissertation is a study in computational auditory scene analysis that seeks to model the role of selective attention in binaural segregation within the framework of a temporal coherence model of auditory streaming. Masks can be obtained by clustering cortical features according to their instantaneous coincidences with pitch and interaural cues. We model selective attention by restrict- ing the ranges of pitch or interaural timing differences used to obtain masks, and evaluate the robustness of the selective attention model in comparison to the baseline model that uses all perceptual cues. Selective attention was as robust to noise and reverberation as the baseline, suggesting the proposed attentive temporal coherence model, in the context of prior experimental findings, may describe the computations by which downstream unattended-speaker representations are suppressed in scene analysis. Finally, the fifth part of this dissertation discusses future directions in studying network interactions in large neural datasets, especially in consideration of current trends towards the adoption of optogenetic stimulation to study neural coding. As a first step in these new directions, a simulation study introducing a reinforcement learning-guided approach to optogenetic stimulation target selection is presented.Item BAYESIAN INFERENCE OF LATENT SPECTRAL AND TEMPORAL NETWORK ORGANIZATIONS FROM HIGH DIMENSIONAL NEURAL DATA(2022) Rupasinghe, Anuththara; Babadi, Behtash; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The field of neuroscience has striven for more than a century to understand how the brain functionally coordinates billions of neurons to perform its many tasks. Recent advancements in neural data acquisition techniques such as multi-electrode arrays, two-photon calcium imaging, and high-speed light-sheet microscopy have significantly contributed to this endeavor's progression by facilitating concurrent observation of spiking activity in large neuronal populations. However, existing methods for network-level inference from these data have several shortcomings: including undermining the non-linear dynamics, ignoring non-stationary brain activity, and causing error propagation by performing inference in a multi-stage fashion. The goal of this dissertation is to close this gap by developing models and methods to directly infer the dynamic spectral and temporal network organizations in the brain, from these ensemble neural data. In the first part of this dissertation, we introduce Bayesian methods to infer dynamic frequency-domain network organizations in neuronal ensembles from spiking observations, by integrating techniques such as point process modeling, state-space estimation, and multitaper spectral estimation. Firstly, we introduce a semi-stationary multitaper multivariate spectral analysis method tailored for neuronal spiking data and establish theoretical bounds on its performance. Building upon this estimator, we then introduce a framework to derive spectrotemporal Granger causal interactions in a population of neurons from spiking data. We demonstrate the validity of these methods through simulations, and applications on real data recorded from cortical neurons of rats during sleep, and human subjects undergoing anesthesia. Finally, we extend these methods to develop a precise frequency-domain inference method to characterize human heart rate variability from electrocardiogram data. The second part introduces a methodology to directly estimate signal and noise correlation networks from two-photon calcium imaging observations. We explicitly model the observation noise, temporal blurring of spiking activities, and other underlying non-linearities in a Bayesian framework, and derive an efficient variational inference method. We demonstrate the validity of the resulting estimators through theoretical analysis and extensive simulations, all of which establish significant gains over existing methods. Applications of our method on real data recorded from the mouse primary auditory cortex reveal novel and distinct spatial patterns in the correlation networks. Finally, we use our methods to investigate how the correlation networks in the auditory cortex change under different stimulus conditions, and during perceptual learning. In the third part, we investigate the respiratory network and the swimming-respiration coordination in larval zebrafish by applying several spectro-temporal analysis techniques, on whole-brain light-sheet microscopy imaging data. Firstly, using multitaper spectrotemporal analysis techniques, we categorize brain regions that are synchronized with the respiratory rhythm based on their distinct phases. Then, we demonstrate that zebrafish swimming is phase-locked to breathing. Next, through the analysis of neural activity and behavior under optogenetic stimulations and two-photon ablations, we identify the brain regions that are key for this swimming-respiration coordination. Finally, using the Izhikevich model for spiking neurons, we develop and simulate a circuit model that replicates this swimming-respiration coupling phenomenon, providing new insights into the possible underlying neural circuitry.Item HIERARCHICAL NEURAL COMPUTATION IN THE MAMMALIAN VISUAL SYSTEM(2015) Cui, Yuwei; Butts, Daniel A; Neuroscience and Cognitive Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Our visual system can efficiently extract behaviorally relevant information from ambiguous and noisy luminance patterns. Although we know much about the anatomy and physiology of the visual system, it remains obscure how the computation performed by individual visual neurons is constructed from the neural circuits. In this thesis, I designed novel statistical modeling approaches to study hierarchical neural computation, using electrophysiological recordings from several stages of the mammalian visual system. In Chapter 2, I describe a two-stage nonlinear model that characterized both synaptic current and spike response of retinal ganglion cells with unprecedented accuracy. I found that excitatory synaptic currents to ganglion cells are well described by excitatory inputs multiplied by divisive suppression, and that spike responses can be explained with the addition of a second stage of spiking nonlinearity and refractoriness. The structure of the model was inspired by known elements of the retinal circuit, and implies that presynaptic inhibition from amacrine cells is an important mechanism underlying ganglion cell computation. In Chapter 3, I describe a hierarchical stimulus-processing model of MT neurons in the context of a naturalistic optic flow stimulus. The model incorporates relevant nonlinear properties of upstream V1 processing and explained MT neuron responses to complex motion stimuli. MT neuron responses are shown to be best predicted from distinct excitatory and suppressive components. The direction-selective suppression can impart selectivity of MT neurons to complex velocity fields, and contribute to improved estimation of the three-dimensional velocity of moving objects. In Chapter 4, I present an extended model of MT neurons that includes both the stimulus-processing component and network activity reflected in local field potentials (LFPs). A significant fraction of the trial-to-trial variability of MT neuron responses is predictable from the LFPs in both passive fixation and a motion discrimination task. Moreover, the choice-related variability of MT neuron responses can be explained by their phase preferences in low-frequency band LFPs. These results suggest an important role of network activity in cortical function. Together, these results demonstrated that it is possible to infer the nature of neural computation from physiological recordings using statistical modeling approaches.Item Single-Microphone Speech Enhancement Inspired by Auditory System(2014) Mirbagheri, Majid; Shamma, Shihab; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Enhancing quality of speech in noisy environments has been an active area of research due to the abundance of applications dealing with human voice and dependence of their performance on this quality. While original approaches in the field were mostly addressing this problem in a pure statistical framework in which the goal was to estimate speech from its sum with other independent processes (noise), during last decade, the attention of the scientific community has turned to the functionality of human auditory system. A lot of effort has been put to bridge the gap between the performance of speech processing algorithms and that of average human by borrowing the models suggested for the sound processing in the auditory system. In this thesis, we will introduce algorithms for speech enhancement inspired by two of these models i.e. the cortical representation of sounds and the hypothesized role of temporal coherence in the auditory scene analysis. After an introduction to the auditory system and the speech enhancement framework we will first show how traditional speech enhancement technics such as wiener-filtering can benefit on the feature extraction level from discriminatory capabilities of spectro-temporal representation of sounds in the cortex i.e. the cortical model. We will next focus on the feature processing as opposed to the extraction stage in the speech enhancement systems by taking advantage of models hypothesized for human attention for sound segregation. We demonstrate a mask-based enhancement method in which the temporal coherence of features is used as a criterion to elicit information about their sources and more specifically to form the masks needed to suppress the noise. Lastly, we explore how the two blocks for feature extraction and manipulation can be merged into one in a manner consistent with our knowledge about auditory system. We will do this through the use of regularized non-negative matrix factorization to optimize the feature extraction and simultaneously account for temporal dynamics to separate noise from speech.Item A spike-based head-movement and echolocation model of the bat superior colliculus(2013) Runchey, Matthew; Horiuchi, Timothy; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Echolocating bats use sonar to sense their environment and hunt for food in darkness. To understand this unusual sensory system from a computational perspective with aspirations towards developing high performance electronic implementations, we study the bat brain. The midbrain superior colliculus (SC) has been shown (in many species) to support multisensory integration and orientation behaviors, namely eye saccades and head turns. Previous computational models of the SC have emphasized the behavior typical to monkeys, barn owls, and cats. Using unique neurobiological data for the bat and incorporating knowledge from other species, a computational spiking model has been developed to produce both head-movement and sonar vocalization. The model accomplishes this with simple neuron equations and synapses, which is promising for implementation on a VLSI chip. This model can serve as a foundation for further developments, using new data from bat experiments, and be easily connected to spiking motor and vocalization systems.