Electrical & Computer Engineering
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Item CORTICAL REPRESENTATIONS OF INTELLIGIBLE AND UNINTELLIGIBLE SPEECH: EFFECTS OF AGING AND LINGUISTIC CONTENT(2023) Karunathilake , I.M Dushyanthi; Simon, Jonathan Z.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Speech communication requires real-time processing of rapidly varying acoustic sounds across various speech landmarks while recruiting complex cognitive processes to derive the intended meaning. Behavioral studies have highlighted that speech comprehension is altered by factors like aging, linguistic content, and intelligibility, yet the systematic neural mechanisms underlying these changes are not well understood. This thesis aims to explore how the neural bases are modulated by each of these factors using three different experiments, by comparing speech representation in the cortical responses, measured by Magnetoencephalography (MEG). We use neural encoding (Temporal Response Functions (TRFs)) and decoding (reconstruction accuracy) models which describe the mapping between stimulus features and the cortical responses, which are instrumental in understanding cortical temporal processing mechanisms in the brain.Firstly, we investigate age-related changes in timing and fidelity of the cortical representation of speech-in-noise. Understanding speech in a noisy environment becomes more challenging with age, even for healthy aging. Our findings demonstrate that some of the age-related difficulties in understanding speech in noise experienced by older adults are accompanied by age-related temporal processing differences in the auditory cortex. This is an important step towards incorporating neural measures to both diagnostic evaluation and treatments aimed at speech comprehension problems in older adults. Next, we investigate how the cortical representation of speech is influenced by the linguistic content by comparing neural responses to four types of continuous speech-like passages: non-speech, non-words, scrambled words, and narrative. We find neural evidence for emergent features of speech processing from acoustics to linguistic processes at the sentential level as incremental steps in the processing of speech input occur. We also show the gradual computation of hierarchical speech features over time, encompassing both bottom-up and top-down mechanisms. Top-down driven mechanisms at linguistic level demonstrates N400-like response, suggesting involvement of predictive coding mechanisms. Finally, we find potential neural markers of speech intelligibility using a priming paradigm, where intelligibility is varied while keeping the acoustic structure constant. Our findings suggest that segmentation of sounds into words emerges with better speech intelligibility and most strongly at ~400 ms in prefrontal cortex (PFC), in line with engagement of top-down mechanisms associated with priming. Taken together, this thesis furthers our understanding on neural mechanisms underlying speech comprehension and potential objective neural markers to evaluate the level of speech comprehension.Item Efficient Machine Learning Techniques for Neural Decoding Systems(2022) wu, xiaomin; Bhattacharyya, Shuvra S.; Chen, Rong; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this thesis, we explore efficient machine learning techniques for calcium imaging based neural decoding in two directions: first, techniques for pruning neural network models to reduce computational complexity and memory cost while retaining high accuracy; second, new techniques for converting graph-based input into low-dimensional vector form, which can be processed more efficiently by conventional neural network models. Neural decoding is an important step in connecting brain activity to behavior --- e.g., to predict movement based on acquired neural signals. Important application areas for neural decoding include brain-machine interfaces and neuromodulation. For application areas such as these, real-time processing of neural signals is important as well as high quality information extraction from the signals. Calcium imaging is a modality that is of increasing interest for studying brain activity. Miniature calcium imaging is a neuroimaging modality that can observe cells in behaving animals with high spatial and temporalresolution, and with the capability to provide chronic imaging. Compared to alternative modalities, calcium imaging has potential to enable improved neural decoding accuracy. However, processing calcium images in real-time is a challenging task as it involves multiple time-consuming stages: neuron detection, motion correction, and signal extraction. Traditional neural decoding methods, such as those based on Wiener and Kalman filters, are fast; however, they are outperformed in terms of accuracy by recently-developed deep neural network (DNN) models. While DNNs provide improved accuracy, they involve high computational complexity, which exacerbates the challenge of real-time processing. Addressing the challenges of high-accuracy, real-time, DNN-based neural decoding is the central objective of this research. As a first step in addressing these challenges, we have developed the NeuroGRS system. NeuroGRS is designed to explore design spaces for compact DNN models and optimize the computational complexity of the models subject to accuracy constraints. GRS, which stands for Greedy inter-layer order with Random Selection of intra-layer units, is an algorithm that we have developed for deriving compact DNN structures. We have demonstrated the effectiveness of GRS to transform DNN models into more compact forms that significantly reduce processing and storage complexity while retaining high accuracy. While NeuroGRS provides useful new capabilities for deriving compact DNN models subject to accuracy constraints, the approach has a significant limitation in the context of neural decoding. This limitation is its lack of scalability to large DNNs. Large DNNs arise naturally in neural decoding applications when the brain model under investigation involves a large number of neurons. As the size of the input DNN increases, NeuroGRS becomes prohibitively expensive in terms of computationaltime. To address this limitation, we have performed a detailed experimental analysis of how pruned solutions evolve as GRS operates, and we have used insights from this analysis to develop a new DNN pruning algorithm called Jump GRS (JGRS). JGRS maintains similar levels of model quality --- in terms of predictive accuracy --- as GRS while operating much more efficiently and being able to handle much larger DNNs under reasonable amounts of time and reasonable computational resources. Jump GRS incorporates a mechanism that bypasses (``jumps over'') validation and retraining during carefully-selected iterations of the pruning process. We demonstrate the advantages and improved scalability of JGRS compared to GRS through extensive experiments in the context of DNNs for neural decoding. We have also developed methods for raising the level of abstraction in the signal representation used for calcium imaging analysis. As a central part of this work, we invented the WGEVIA (Weighted Graph Embedding with Vertex Identity Awareness) algorithm, which enables DNN-based processing of neuron activity that is represented in the form of microcircuits. In contrast to traditional representations of neural signals, which involve spiking signals, a microcircuit representation is a graphical representation. Each vertex in a microcircuit corresponds to a neuron, and each edge carries a weight that captures information about firing relationships between the neurons associated with the vertices that are incident to the edge. Our experiments demonstrate that WGEVIA is effective at extracting information from microcircuits. Moreover,raising the level of abstraction to microcircuit analysis has the potential to enable more powerful signal extraction under limited processing time and resources.