Active processes, morphology, and dynamics of icy debris fans: Landform evolution along rapidly degrading escarpments in alpine regions undergoing recent deglaciation

In the past few decades the impacts of climate warming have been significant in alpine glaciated regions. Many valley glaciers formerly linked as distributary glaciers to high-level icecaps have decoupled at their icefalls, exposing major escarpments and generating a suite of dynamic landforrns dominated by mass wasting. Ice-dominated landforms, here termed icy debris fans, develop rapidly by ice avalanching, rockfall, and icy debris flow. Field-based reconnaissance studies at two alpine settings, the Wrangell Mountains of Alaska and the Southern Alps of New Zealand, provide a preliminary morphogenetic model of spatial and temporal evolution of icy debris fans in a range of alpine settings. The influence of these processes on landform evolution is largely unrecognized in the literature dealing with post-glacial landform adjustment known as the paraglacial. A better understanding of these dynamic processes will be increasingly important because of the extreme geohazards characterizing these areas. Our field studies show that after glacier decoupling, icy debris fans begin to form along the base of bedrock escarpments at the mouths of catchments and prograde over valley glaciers. The presence of a distinct catchment, apex, and fan morphology distinguishes these landforms from other landforms common in periglacial hillslope settings receiving abundant clastic debris and ice. Ice avalanching is the most abundant process involved in icy debris fan formation. Fans developed below weakly incised catchments are dominated by ice avalanching and are composed primarily of ice with minor lithic detritus. Typically, avalanches fall into the fan catchments where sediments transform into grainflows that flow onto the fans. Once on the fans, avalanche deposits ablate rapidly, flattening and concentrating lithic fragments at the surface. Icy debris fans may become thick enough to become glaciers with splay crevasse systems. Fans developed below larger, more complex catchments are composed of higher proportions of lithic detritus resulting from temporary storage of ice and lithic detritus deposits within the catchment. Episodic outbursts of meltwater from the icecap may mix with the stored sediments and mobilize icy debris flows (mixture of ice and lithic clasts) onto the fans. Our observations indicate that the entire evolutionary cycle of icy debris fans probably occurs during an early paraglacial interval (i.e., decades to 100 years). Observations comparing avalanche frequency, volume, and fan morphologic evolution at the Alaska site between 2006 and 2010 illustrate complex response between icy debris fans even within the same cirque - where one fan may be growing while others are downwasting because of differences in ice supply controlled by their respective catchments and icecap contributions. As ice supply from the icecap diminishes through time, icy debris fans rapidly downwaste and eventually evolve into talus cones that receive occasional but ephemeral ice avalanches.

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.