A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Noninvasive neural decoding of overt and covert hand movement(2010) Bradberry, Trent Jason; Contreras-Vidal, José L.; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)It is generally assumed that the signal-to-noise ratio and information content of neural data acquired noninvasively via magnetoencephalography (MEG) or scalp electroencephalography (EEG) are insufficient to extract detailed information about natural, multi-joint movements of the upper limb. If valid, this assumption could severely limit the practical usage of noninvasive signals in brain-computer interface (BCI) systems aimed at continuous complex control of arm-like prostheses for movement impaired persons. Fortunately this dissertation research casts doubt on the veracity of this assumption by extracting continuous hand kinematics from MEG signals collected during a 2D center-out drawing task (Bradberry et al. 2009, NeuroImage, 47:1691-700) and from EEG signals collected during a 3D center-out reaching task (Bradberry et al. 2010, Journal of Neuroscience, 30:3432-7). In both studies, multiple regression was performed to find a matrix that mapped past and current neural data from multiple sensors to current hand kinematic data (velocity). A novel method was subsequently devised that incorporated the weights of the mapping matrix and the standardized low resolution electromagnetic tomography (sLORETA) software to reveal that the brain sources that encoded hand kinematics in the MEG and EEG studies were corroborated by more traditional studies that required averaging across trials and/or subjects. Encouraged by the favorable results of these off-line decoding studies, a BCI system was developed for on-line decoding of covert movement intentions that provided users with real-time visual feedback of the decoder output. Users were asked to use only their thoughts to move a cursor to acquire one of four targets on a computer screen. With only one training session, subjects were able to accomplish this task. The promising results of this dissertation research significantly advance the state-of-the-art in noninvasive BCI systems.Item Representation of speech in the primary auditory cortex and its implications for robust speech processing(2008-08-05) Mesgarani, Nima; Shamma, Shihab; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Speech has evolved as a primary form of communication between humans. This most used means of communication has been the subject of intense study for years, but there is still a lot that we do not know about it. It is an oft repeated fact, that even the performance of the best speech processing algorithms still lags far behind that of the average human, It seems inescapable that unless we know more about the way the brain performs this task, our machines can not go much further. This thesis focuses on the question of speech representation in the brain, both from a physiological and technological perspective. We explore the representation of speech through the encoding of its smallest elements - phonemic features - in the primary auditory cortex. We report on how population of neurons with diverse tuning properties respond discriminately to phonemes resulting in explicit encoding of their parameters. Next, we show that this sparse encoding of the phonemic features is a simple consequence of the linear spectro-temporal properties of the auditory cortical neurons and that a Spectro-Temporal receptive field model can predict similar patterns of activation. This is an important step toward the realization of systems that operate based on the same principles as the cortex. Using an inverse method of reconstruction, we shall also explore the extent to which phonemic features are preserved in the cortical representation of noisy speech. The results suggest that the cortical responses are more robust to noise and that the important features of phonemes are preserved in the cortical representation even in noise. Finally, we explain how a model of this cortical representation can be used for speech processing and enhancement applications to improve their robustness and performance.Item Bio-inspired VLSI Systems: from Synapse to Behavior(2008-08-04) Xu, Peng; Abshire, Pamela; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)We investigate VLSI systems using biological computational principles. The elegance of biological systems throughout the structure levels provides possible solutions to many engineering challenges. Specifically, we investigate neural systems at the synaptic level and at the sensorimotor integration level, which inspire our similar implementations in silicon. For both VLSI systems, we use floating gate MOSFETs in standard CMOS processes as nonvolatile storage elements, which enable adaptation and programmability. We propose a compact silicon stochastic synapse and methods to incorporate activity-dependent dynamics, which emulate a biological stochastic synapse. We implement and demonstrate the first silicon stochastic synapse with short-term depression by modulating the influence of noise on the circuit. The circuit exhibits true randomness and similar behavior of rate normalization and information redundancy reduction as its biological counterparts. The circuit behavior also agrees well with the theory and simulation of a circuit model based on a subtractive single release model. To understand the stochastic behavior of the silicon stochastic synapse and the stochastic operation of conventional circuits due to semiconductor technology scaling, we develop the stochastic modeling of circuits and transient analysis from the numerical solution of the stochastic model. The analytical solution of steady state distribution could be obtained from first principles. Small signal stochastic models show the interaction between noise and circuit dynamics, elucidating the effect of device parameters and biases on the stochastic behavior. We investigate optic flow wide field integration based navigation inspired from the fly in simulation, theory, and VLSI design. We generalize the framework to limited view angles. We design and test an integrated motion image sensor with on-chip optic flow estimation, adaptation, and programmable spatial filtering to directly interface with actuators for autonomous navigation. This is the first reported image sensor that uses the spatial motion pattern to extract motion parameters enabled by the mismatch compensation and programmable filters. The sensor is integrated with a ground vehicle and navigation through simple tunnel environments is demonstrated. It provides light weight and low power integrated approach to autonomous navigation of micro air vehicles.Item Hearing VS. Listening: Attention Changes the Neural Representations of Auditory Percepts(2008-05-01) xiang, juanjuan; Simon, Jonathan Z.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Making sense of acoustic environments is a challenging task. At any moment, the signals from distinct auditory sources arrive in the ear simultaneously, forming an acoustic mixture. The brain must represent distinct auditory objects in this complex scene and prioritize processing of relevant stimuli while maintaining the capability to react quickly to unexpected events. The present studies explore neural representations of temporal modulations and the effects of attention on these representations. Temporal modulation plays a significant role in speech perception and auditory scene analysis. To uncover how temporal modulations are processed and represented is potentially of great importance for our general understanding of the auditory system. Neural representations of compound modulations were investigated by magnetoencephalography (MEG). Interaction components are generated by near rather than distant modulation rhythms, suggesting band-limited modulation filter banks operating in the central stage of the auditory system. Furthermore, the slowest detectable neural oscillation in the auditory cortex corresponds to the perceived oscillation of the auditory percept. Interactions between stimulus-evoked and goal-related neural responses were investigated in simultaneous behavioral-neurophysiological studies, in which we manipulate subjects' attention to different components of an auditory scene. Our experimental results reveal that attention to the target correlates with a sustained increase in the neural target representation, beyond well-known transient effects. The enhancement of power and phase coherence presumably reflects increased local and global synchronizations in the brain. Furthermore, the target's perceptual detectability improves over time (several seconds), correlating strongly with the target representation's neural buildup. The change in cortical representations can be reversed in a short time-scale (several minutes) by various behavioral goals. These aforementioned results demonstrate that the neural representation of the percept is encoded using the feature-driven mechanisms of sensory cortex, but shaped in a sustained manner via attention-driven projections from higher-level areas. This adaptive neural representations occur on multiple time scales (seconds vs. minutes) and multiple spatial scales (local vs. global synchronization). Such multiple resolutions of adaptation may underlie general mechanisms of scene organization in any sensory modality and may contribute to our highly adaptive behaviors.Item Bat azimuthal echolocation using interaural level differences: modeling and implementation by a VLSI-based hardware system(2006-07-31) Shi, Zhiping; Horiuchi, Timothy K.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Bats have long fascinated both scientists and engineers due to their superb ability to use echolocation to fly with speed and agility through complex natural environments in complete darkness. This dissertation presents a neuromorphic VLSI circuit model of bat azimuthal echolocation. Interaural level differences (ILDs) are the cues for bat azimuthal echolocation and are also the primary cues used by other mammals to localize high frequency sounds. The fact that neurons in bats respond to short echoes by one or two spikes strongly suggests that the conventionally used firing rate is an unlikely code. The operation of first spike latency in ILD computation and transformation is investigated in a network of spiking neurons linking the lateral superior olive (LSO), dorsal nucleus of the lateral lemniscus (DNLL), and inferior colliculus (IC). The results of the investigation suggest that spatially distributed first spike latencies can serve as a fast code for azimuth that can be ``read-out'' by ascending stages. With the hardware echolocation model that uses spike timing representation, we study how multiple echoes can affect bat echolocation and demonstrate that the response to multiple sounds is not a simple linear addition of the response to single sounds. By developing functional models of the bat echolocation system, we can study the efficient implementation demonstrated by nature. For example, variations among analog VLSI circuit units due to the unavoidable transistor mismatch - traditionally thought of as a hurdle to overcome - have been found beneficial in generating the desired diversity of response that is similar to their neural counterparts. This work advocates the use and design of summating and exponentially decaying synapses. A compact and easily controllable synapse circuit has found an application in achieving a linear temporal spike summation by operating with a very short time constants. It has also been applied in modeling a nonlinear intensity-latency trading by working with a long synaptic time constant. We propose a new synapse circuit model that is compatible with those used in computational models and implementable by CMOS transistors operating in the subthreshold region.