Learning is central to infusing intelligence to any biologically inspired system. This study introduces a novel Cross-Correlated Delay Shift (CCDS) learning method for spiking neurons with the ability to learn and reproduce arbitrary spike patterns in a supervised fashion with applicability tospatiotemporalinformation encoded at the precise timing of spikes. By integrating the cross-correlated term,axonaland synapse delays, the CCDS rule is proven to be both biologically plausible and computationally efficient. The proposed learning algorithm is evaluated in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. The results indicate that the proposed CCDS learning rule greatly improves classification accuracy when compared to the standards reached with the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. Network structureis the crucial partforany application domain of Artificial Spiking Neural Network (ASNN). Thus, temporal learning rules in multilayer spiking neural networks are investigated. As extensions of single-layer learning rules, the multilayer CCDS (MutCCDS) is also developed. Correlated neurons are connected through fine-tuned weights and delays. In contrast to the multilayer Remote Supervised Method (MutReSuMe) and multilayertempotronrule (MutTmptr), the newly developed MutCCDS shows better generalization ability and faster convergence. The proposed multilayer rules provide an efficient and biologically plausible mechanism, describing how delays and synapses in the multilayer networks are adjusted to facilitate learning. Interictalspikes (IS) aremorphologicallydefined brief events observed in electroencephalography (EEG) records from patients with epilepsy. The detection of IS remains an essential task for 3D source localization as well as in developing algorithms for seizure prediction and guided therapy. In this work, we present a new IS detection method using the Wavelet Encoding Device (WED) method together with CCDS learning rule and a specially designed Spiking Neural Network (SNN) structure. The results confirm the ability of such SNN to achieve good performance for automatically detecting such events from multichannel EEG records.

Surface electromyography records the motor unit action potential signals in the vicinity of the electrode to reveal information on muscle activation. Decomposition of sEMG signals for characterization of constituent motor unit action potentials in terms of amplitude and firing times is useful for clinical research as well as diagnosis of neurological disorders. Successful decomposition of sEMG signals would allow for pertinent motor unit action potential information to be acquired without discomfort to the subject or the need for a well-trained operator (compared with intramuscular EMG). To determine amplitudes and firing times for motor unit action potentials in an sEMG recording, Szlavik's perturbation based decomposition may be applied. The decomposition was initially applied to synthetic sEMG signals and then to experimental data collected from the biceps brachii. Szlavik's decomposition estimator yields satisfactory results for synthetic and experimental sEMG signals with reasonable complexity.

Since the 1900s, the number of deaths attributable to cardiovascular disease has steadily risen. With the advent of antihypertensive drugs and non-invasive surgical procedures, such as intravascular stenting, these numbers have begun to level off. Despite this trend, the number of patients diagnosed with some form of cardiovascular disease has only increased. By 2030, prevalence of coronary heart disease is expected to increase approximately by 18% in the United States. By 2050, prevalence of peripheral arterial occlusive disease is expected to increase approximately by 98% in the U.S. No single drug or surgical intervention offers a complete solution to these problems. Thus, a multi-faceted regimen of lifestyle changes, medication, and device or surgical interventions is usually necessary. A potential adjunct therapy and cost-effective solution for treating cardiovascular disease that has been overlooked is neurostimulation. Recent studies show that using neurostimulation techniques, such as transcutaneous electrical nerve stimulation (TENS), can help to reduce ischemic pain, lower blood pressure, increase blood flow to the periphery, and decrease systemic vascular resistance. The mechanisms by which these hemodynamic changes occur is still under investigation. The primary aim of this thesis is to elucidate these mechanisms through a thorough synthesis of the existing literature on this subject. Neurostimulation, specifically TENS, is thought to modulate both the metaboreflex and norepinephrine release from sympathetic nerve terminals. To test the hypothesis that TENS increases local blood flow, decreases mean arterial pressure, and decreases cutaneous vascular resistance compared to placebo, in which the electrodes are attached but no electrical stimulation is applied, a protocol was developed to test the effect of neurostimulation on healthy subjects. Implementation of this protocol in a pilot study will determine if neurostimulation causes significant changes in blood flow using the most relevant perfusion measurement instrumentation. Before conducting this study, pre-pilot comparison studies of interferential current therapy (IFC) versus TENS, low frequency (4 Hz) TENS versus high frequency (100 Hz) TENS, and electrode placement on the back versus the forearm were conducted. The only statistically significant difference found was that the application of IFC on the back decreased the reperfusion time, meaning that the time required to reach the average baseline perfusion unit value after occlusion decreased. Further pre-pilot work investigating these different modalities and parameters is necessary to ensure that favorable hemodynamic changes can be detected in the pilot study.

This dissertation introduces a novel approach at gauging patterns of informa- tion flow using brain connectivity analysis and partial directed coherence (PDC) in epilepsy. The main objective of this dissertation is to assess the key characteristics that delineate neural activities obtained from patients with epilepsy, considering both focal and generalized seizures. The use of PDC analysis is noteworthy as it es- timates the intensity and direction of propagation from neural activities generated in the cerebral cortex, and it ascertains the coefficients as weighted measures in formulating the multivariate autoregressive model (MVAR). The PDC is used here as a feature extraction method for recorded scalp electroencephalograms (EEG) as means to examine the interictal epileptiform discharges (IEDs) and reflect the phys- iological changes of brain activity during interictal periods. Two experiments were set up to investigate the epileptic data by using the PDC concept. For the investigation of IEDs data (interictal spike (IS), spike and slow wave com- plex (SSC), and repetitive spikes and slow wave complex (RSS)), the PDC analysis estimates the intensity and direction of propagation from neural activities gener- ated in the cerebral cortex, and analyzes the coefficients obtained from employing MVAR. Features extracted by using PDC were transformed into adjacency matrices using surrogate data analysis and were classified by using the multilayer Perceptron (MLP) neural network. The classification results yielded a high accuracy and pre- cision number. The second experiment introduces the investigation of intensity (or strength) of information flow. The inflow activity deemed significant and flowing from other regions into a specific region together with the outflow activity emanating from one region and spreading into other regions were calculated based on the PDC results and were quantified by the defined regions of interest. Three groups were considered for this study, the control population, patients with focal epilepsy, and patients with generalized epilepsy. A significant difference in inflow and outflow validated by the nonparametric Kruskal-Wallis test was observed for these groups. By taking advantage of directionality of brain connectivity and by extracting the intensity of information flow, specific patterns in different brain regions of interest between each data group can be revealed. This is rather important as researchers could then associate such patterns in context to the 3D source localization where seizures are thought to emanate in focal epilepsy. This research endeavor, given its generalized construct, can extend for the study of other neurological and neurode- generative disorders such as Parkinson, depression, Alzheimers disease, and mental illness.

In previous studies it was determined that the fiber diameter distribution in a peripheral nerve could be estimated by a simulation technique known as group delay. These results could be further improved using a combinatorial optimization algorithm called simulated annealing. This paper explores the structure and behavior of simulated annealing for the application of optimizing the group delay estimated fiber diameter distribution. Specifically, a set of parameters known as the cooling schedule is investigated to determine its effectiveness in the optimization process. Simulated annealing is a technique for finding the global minimum (or maximum) of a cost function which may have many local minima. The set of parameters which comprise the cooling schedule dictate the rate at which simulated annealing reaches its final solution. Converging too quickly can result in sub-optimal solutions while taking too long to determine a solution can result in an unnecessarily large computational effort that would be impractical in a real-world setting. The goal of this study is to minimize the computational effort of simulated annealing without sacrificing its effectiveness at minimizing the cost function. The cost function for this application is an error value computed as the difference in the maximum compound evoked potentials between an empirically-determined template distribution of fiber diameters and an optimized set of fiber diameters. The resulting information will be useful when developing the group delay estimation and subsequent simulated annealing optimization in an experimental laboratory setting.

Hypertrophic cardiomyopathy (HCM) is the most common monogenic disorder and the leading cause of sudden arrhythmic death in children and young adults. It is typically asymptomatic and first manifests itself during cardiac arrest, making it a challenge to diagnose in advance. Computational models can explore and reveal underlying molecular mechanisms in cardiac electrophysiology by allowing researchers to alter various parameters such as tissue size or ionic current amplitudes. The goal of this thesis is to develop a computational model in MATLAB and to determine if this model can accurately indicate cases of hypertrophic cardiomyopathy. This goal is achieved by combining a three-dimensional network of the bidomain model with the Beeler-Reuter model and then by manually varying the thickness of that tissue and recording the resulting membrane potential with respect to time. The results of this analysis demonstrated that the developed model is able to depict variations in tissue thickness through the difference in membrane potential recordings. A one-way ANOVA analysis confirmed that the membrane potential recordings of the different thicknesses were significantly different from one another. This study assumed continuum behavior, which may not be indicative of diseased tissue. In the future, such a model might be validated through in vitro experiments that measure electrical activity in hypertrophied cardiac tissue. This model may be useful in future applications to study the ionic mechanisms related to hypertrophic cardiomyopathy or other related cardiac diseases.

Peripheral arterial occlusive disease (PAOD) affects 8 to 12 million Americans over the age of 50. As the disease progresses, arterial occlusions arising from atherosclerotic lesions inhibit normal metabolic vasodilation in the peripheries, resulting in limb ischemia and claudication. Pharmacological and surgical treatments currently used to treat both the hemodynamic and pain symptoms associated with PAOD can involve adverse and potentially life-threatening side effects. Thus, there is a need for additional innovative therapies for PAOD. Neurostimulation has a known analgesic effect on both acute and chronic pain. Although the exact mechanisms remain under investigation, local vascular tone may be modulated by neurostimulation in addition to pain modulation. The Gate Control Theory proposes that electrical activation of mechanoreceptive afferent somatosensory nerves, specifically Aβ fibers, inhibits pain signaling to the brain by activating an inhibitory interneuron in the dorsal horn of the spinal cord which dampens signaling from afferent, C type peripheral nociceptor nerves. Interestingly, Aβ fiber activation may also inhibit norepinephrine release from sympathetic nerve terminals on efferent neurons by activating α-2 adrenergic receptors along the same dermatome, resulting in localized vasodilation in both limbs. Ultimately, electrical stimulation may decrease mean blood pressure and increase local blood flow. The focus of this study was to optimize protocols and perform a small scale clinical study to investigate hemodynamic and analgesic responses to neurostimulation during acute ischemia. We hypothesized that ganglial transcutaneous electrical neurostimulation (TENS) and interferential current (IFC) treatments would decrease pain perception and vascular resistance in the periphery in young, healthy subjects. We further hypothesized that IFC may have a greater hyperemic and analgesic effect on acute ischemia than TENS as its current waveform may be more efficient at overcoming skin impedance. Interestingly, we found trends suggesting that TENS and IFC may increase vascular resistance (VR) and have no noticeable analgesic effect, though TENS may have a slightly lower increase in VR associated with an increase in pain. Further work characterizing the hemodynamic effects of different stimulus waveforms is needed to inform future research into possible neuromodulation therapies for ischemic disease.

This work focuses on modeling a demyelinated Hodgkin and Huxley (HH) neuron with Simulated Program with Integrated Circuit Emphasis (SPICE) platform. Demyelinating disorders affect over 350,000 people in the U.S and understanding the demyelination process at the cellular level is necessary to find safe ways to treat the diseases [9]. Utilizing a previous SPICE model of an electrically small cell neuron developed by Szlavik [32], an extended core conductor myelinated neuron was produced in this work. The myelinated neuron developed has seven active Nodes of Ranvier (nodes) separated by a myelin sheath. The myelin sheath can be successfully modeled with a resistive and capacitive network known as internodes. Both the Nodes of Ranvier and internode equivalent circuits were implemented in P-SPICE sub-circuit library files. Properties of the neuron can be changed in the library files to simulate neurons of different electrical or geometric properties. Using the P-SPICE code developed in this work, a myelinated neuron’s action potential was simulated and the action potential at each node was recorded. The action potential at each node was uniform in amplitude and pulse width. The conduction velocity of the action potential was calculated to be 57.15 m/s. Demyelination can be modeled by decreasing the capacitance and increasing the resistance of the myelin [34]. Two demyelinated neuron models were simulated in this work. The first model had one internode segment demyelinated, and the second model was of three consecutive internode segments. The resulting conduction velocity was calculated for both simulations. For one and three internode segment demyelinated the conduction velocity was slowed to 44.15 m/s, and 27.15 m/s respectively. This model successfully showed that an HH neuron implemented in SPICE could show the effects of demyelination on conduction velocity The goal of this work is to develop a demyelinated neuron so that treatments for Multiple Sclerosis (MS) and other demyelinated neurons could be simulated to test various treatments’ effectiveness. A current treatment for MS is ion channel blockers. Future work would be to use this model to test current ion channel blocker therapy and to validate if such therapies alleviate conduction slowing.

Parkinson's Disease is characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). The SNc supplies the basal ganglia (BG) via dopaminergic projections which innervate D1 and D2 receptors that mediate motor control. The BG also mediates cognitive processes and eye movement, parallel to its involvement in motor control. Behavioural correlates of PD have been established from previous countermanding tasks and population neural activity has been shown to correlate with PD disease state, but a reliable means to find patient-specific biomarkers of disease remains unknown. Here, we propose using eye movements and electroencephalography (EEG) to capture neural correlates of dysfunction in PD. We have developed a novel saccade-based stop-signal task in VR that probes the subject's ability to recruit the neural processes involved in action selection and response inhibition. We have tested this system on 7 healthy subjects and verified that we could identify key signature changes in the EEG profile during left and right saccade, countermand, and antisaccades similar to those found in similar reach tasks. The successful completion of a countermand (revoking a planned action) stop trial requires large synchronization of frontal theta and motor beta activity, representing the BG-thalamocortical loop recruiting the necessary processes to inhibit motor responses. The pattern in the event-related potentials that illustrates this is a strong event-related synchronization (ERS) peak followed by an event-related desynchronization (ERD) dip, and increased weights in the scalp topology at the frontal-parietal region. Since tasks involving response inhibition serve to probe the subject’s ability to revoke a planned action, it does not matter whether the task was completed using hand movements or saccades. Our ERP isolated from Independent Component Analysis (ICA) resembles the ERP from previous literature, and exhibits increased weights on the sensorimotor region with a narrow band beta. This narrow band beta range is subject-specific and can be better visualized by using a modelling approach called FOOOF (fitting oscillations one over f). Lastly, the increased decoding performance in each subject's successive recording session suggests that using subject-specific features positively biases the model towards enhanced generalizability. Our experimental platform provides a robust framework that accounts for trial-by-trial variability, and can capture the presence of and evoke beta oscillations in healthy subjects.

In this work, a hydrogel made from hyaluronic acid is synthesized and utilized to study neural stem cell behavior within a custom tailored three dimensional microenvironment. The physical properties of the hydrogel have been optimized to create an environment conducive for neural stem cell differentiation by mimicking the native brain extracellular matrix (ECM) environment. The physical properties characterized include degree of methacrylation, swelling ratios, enzymatic degradation rates, and viscoelastic moduli. One dimensional proton nuclear magnetic resonance (1HNMR) confirms modification of the hyaluronic acid polymers, and is used to quantify the degree of methacrylation. Rheological measurements are made to quantify the viscoelastic moduli. Further post-processing by lyophilization leads to generation of large voids to facilitate re-swelling and cell infiltration. ReNcell VM (RVM), and adult human neural stem cell line derived from the ventral mesencephalon, are cultured and differentiated inside the hydrogel for up to 2 weeks. Differentiation is characterized by immunocytochemistry (ICC) and real time quantitative polymerase chain reaction (qRT-PCR). / Bioengineering