Efficient and Accurate Characterization of the Bergman Cyclization for an Expanded Substructure of Esperamicin A1

Incorporation of enediynes into anticancer drugs remains an intriguing yet elusive strategy for the design of therapeutically active agents. Density functional theory was used to locate reactants, products, and transition states along the Bergman cyclization pathways connecting enediynes to reactive para-biradicals. Sum method correction to low-level calculations confirmed B3LYP/6-31G(d,p) as the method of choice in investigating enediynes. Herein described as MI:Sum, calculated reaction enthalpies differed from experiment by an average of 2.1 kcal·mol−1 (mean unsigned error). A combination of strain energy released across the reaction coordinate and the critical intramolecular distance between reacting diynes explains reactivity differences. Where experimental and calculated barrier heights are in disagreement, higher level multireference treatment of the enediynes confirms lower level estimates. Previous work concerning the chemically reactive fragment of esperamcin, MTC, is expanded to our model system MTC2.

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.