Accordion-optimized DEPT experiments

In this contribution, a pulse sequence is described for recording accordion-optimized DEPT experiments. The proposed ACCORDEPT experiment detects a wide range of one-bond coupling constants using accordion optimization. As a proof of concept, this strategy has been applied to a mesogen containing a large range of one-bond (1)J(CH) coupling constants associated with the various structural elements. The ACCORDEPT experiment afforded significant enhancements for the resonances with the larger 1JCH couplings, similar SNR for aliphatic resonances, but reduced SNR for aliphatic resonances as compared with the standard DEPT experiment. In addition, the ACCORDEPT is straightforward to implement, does not require any supplementary calibration procedures and can be used under automated conditions without difficulty by inexperienced users. Copyright (C) 2010 John Wiley & Sons, Ltd.

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.