UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
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 given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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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 EXTRACTING NEURONAL DYNAMICS AT HIGH SPATIOTEMPORAL RESOLUTIONS: THEORY, ALGORITHMS, AND APPLICATION(2018) Sheikhattar, Alireza; Babadi, Behtash; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Analyses of neuronal activity have revealed that various types of neurons, both at the single-unit and population level, undergo rapid dynamic changes in their response characteristics and their connectivity patterns in order to adapt to variations in the behavioral context or stimulus condition. In addition, these dynamics often admit parsimonious representations. Despite growing advances in neural modeling and data acquisition technology, a unified signal processing framework capable of capturing the adaptivity, sparsity and statistical characteristics of neural dynamics is lacking. The objective of this dissertation is to develop such a signal processing methodology in order to gain a deeper insight into the dynamics of neuronal ensembles underlying behavior, and consequently a better understanding of how brain functions. The first part of this dissertation concerns the dynamics of stimulus-driven neuronal activity at the single-unit level. We develop a sparse adaptive filtering framework for the identification of neuronal response characteristics from spiking activity. We present a rigorous theoretical analysis of our proposed sparse adaptive filtering algorithms and characterize their performance guarantees. Application of our algorithms to experimental data provides new insights into the dynamics of attention-driven neuronal receptive field plasticity, with a substantial increase in temporal resolution. In the second part, we focus on the network-level properties of neuronal dynamics, with the goal of identifying the causal interactions within neuronal ensembles that underlie behavior. Building up on the results of the first part, we introduce a new measure of causality, namely the Adaptive Granger Causality (AGC), which allows capturing the sparsity and dynamics of the causal influences in a neuronal network in a statistically robust and computationally efficient fashion. We develop a precise statistical inference framework for the estimation of AGC from simultaneous recordings of the activity of neurons in an ensemble. Finally, in the third part we demonstrate the utility of our proposed methodologies through application to synthetic and real data. We first validate our theoretical results using comprehensive simulations, and assess the performance of the proposed methods in terms of estimation accuracy and tracking capability. These results confirm that our algorithms provide significant gains in comparison to existing techniques. Furthermore, we apply our methodology to various experimentally recorded data from electrophysiology and optical imaging: 1) Application of our methods to simultaneous spike recordings from the ferret auditory and prefrontal cortical areas reveals the dynamics of top-down and bottom-up functional interactions underlying attentive behavior at unprecedented spatiotemporal resolutions; 2) Our analyses of two-photon imaging data from the mouse auditory cortex shed light on the sparse dynamics of functional networks under both spontaneous activity and auditory tone detection tasks; and 3) Application of our methods to whole-brain light-sheet imaging data from larval zebrafish reveals unique insights into the organization of functional networks involved in visuo-motor processing.