Contributors:Sigillito, Anthony James, Lyon, Stephen A, Electrical Engineering Department
Contributors:Puissant, Stéphane, Lee, Young June
FIGURE 6. Hyalessa maculaticollis. Echemes structure. A, Power frequency spectrum represented with overlay of 52 spectra computed from echemes with high amplitude oscillations showing a dominant frequency marked by F3. B, Detailed oscillogram showing the first echeme with low amplitude oscillations and the second echeme with high amplitude oscillations. C, Power frequency spectrum represented with overlay of 71 spectra computed from echemes with low amplitude oscillations showing dominant frequencies marked by F1 and F2.
FIGURE 8. Hyalessa fuscata. Echemes structure. A, Power frequency spectrum represented with overlay of 47 spectra computed from echemes with high amplitude oscillations showing dominant frequencies marked by F1, F2, F3 and F4. B, Detailed oscillogram showing the first echeme with low amplitude oscillations and the second echeme with high amplitude oscillations. C, Power frequency spectrum represented with overlay of 38 spectra computed from echemes with low amplitude oscillations showing dominant frequencies marked by F1 and F2.
The pursuit of novel functional building blocks for the emerging field of quantum computing is one of the most appealing topics in the context of quantum technologies. Herein we showcase the urgency of introducing peptides as versatile platforms for quantum computing. In particular, we focus on lanthanide-binding tags, originally developed for the study of protein structure. We use pulsed electronic paramagnetic resonance to demonstrate quantum coherent oscillations in both neodymium and gadolinium peptidic qubits. Calculations based on density functional theory followed by a ligand field analysis indicate the possibility of influencing the nature of the spin qubit states by means of controlled changes in the peptidic sequence. We conclude with an overview of the challenges and opportunities opened by this interdisciplinary field.
The current variety of treatment options for epilepsy leaves 30% of those who suffer from this chronic neurological disease without a cure. Therefore, this senior thesis project aims to uncover new insights about the brain structure that underlies susceptibility to epilepsy in hopes that a greater understanding of this underlying structure will catalyze the discovery of novel therapeutic methods which target these underlying differences in brain structure. To drive the discovery of new insights about underlying structure, this project addresses the following tension found in the literature: high frequencyoscillations occur in both the brains of those with epilepsy and in the brains of those without epilepsy. Only when high frequencyoscillations occur in the brains of those with epilepsy does the brain enter a state of unstable dynamics and seizure activity. This suggests that there is a difference in underlying structure between epileptic and non-epileptic brains, and this study uses computational modeling of neuronal firing to characterize these differences.
First, based on a firing rate model, we find that within the phase space of the weight values, there is a band of stability from which one might predict the stability of a set of weights. Then, in the next two versions of the model, we add Hebbian plasticity and homeostatic plasticity. Only through the addition of Hebbian plasticity and homeostatic plasticity does high frequencyoscillation, the manipulation described in our driving question, have a lasting effect on the weights. With the addition of a rate based Hebbian plasticity model to the base firing rate model, we find that weights can be perturbed from this band of stability through Hebbian plasticity. Adding a weight based homeostatic plasticity model to the base firing rate and Hebbian plasticity model then gives insight into the fact that having a target weight within a certain location with respect to the band of stability can rescue stability of a set of original weights from the destabilizing effects of Hebbian plasticity. Finally, we explore the effect of high frequencyoscillation on various weight combinations within the phase space, and we find that certain weight combinations are projected to an unstable state through high frequencyoscillation while other weight combinations remain at a stable state even in the face of high frequencyoscillation. The unifying characteristic of those weights which remain stable in the face of high frequencyoscillation remains an open question. However, in the process of investigating high frequencyoscillations, it was found that weights on the edge of the band of stability are more robust to instability through Hebbian plasticity than weights on the band of stability that are further from the edge.
These results suggest that the differential response to high frequencyoscillation between epileptic and non-epileptic brains can be attributed at least in part to the location of weights with respect to the band of stability.