Terrestrial feeding in aquatic turtles: environment-dependent feeding behavior modulation and the evolution of terrestrial feeding in Emydidae

Evolutionary transitions between aquatic and terrestrial environments are common in vertebrate evolution. These transitions require major changes in most physiological functions, including feeding. Emydid turtles are ancestrally aquatic, with most species naturally feeding only in water, but some terrestrial species can modulate their feeding behavior appropriately for both media. In addition, many aquatic species can be induced to feed terrestrially. A comparison of feeding in both aquatic and terrestrial environments presents an excellent opportunity to investigate the evolution of terrestrial feeding from aquatic feeding, as well as a system within which to develop methods for studying major evolutionary transitions between environments. Individuals from eight species of emydid turtles (six aquatic, two terrestrial) were filmed while feeding underwater and on land. Bite kinematics were analyzed to determine whether aquatic turtles modulated their feeding behavior in a consistent and appropriate manner between environments. Aquatic turtles showed consistent changes between environments, taking longer bites and using more extensive motions of the jaw and hyoid when feeding on land. However, these motions differ from those shown by species that naturally feed in both environments and mostly do not seem to be appropriate for terrestrial feeding. For example, more extensive motions of the hyoid are only effective during underwater suction feeding. Emydids evolving to feed on land probably would have needed to evolve or learn to overcome many, but not all, aspects of the intrinsic emydid response to terrestrial feeding. Studies that investigate major evolutionary transitions must determine what responses to the new environment are shown by naïve individuals in order to fully understand the evolutionary patterns and processes associated with these transitions.

Temperature-Dependent Defect Dynamics in the Network Glass SiO2

We investigate the long time dynamics of a strong glass former, SiO2, below the glass transition temperature by averaging single-particle trajectories over time windows which comprise roughly 100 particle oscillations. The structure on this coarse-grained time scale is very well defined in terms of coordination numbers, allowing us to identify ill-coordinated atoms, which are called defects in the following. The most numerous defects are O-O neighbors, whose lifetimes are comparable to the equilibration time at low temperature. On the other hand, SiO and OSi defects are very rare and short lived. The lifetime of defects is found to be strongly temperature dependent, consistent with activated processes. Single-particle jumps give rise to local structural rearrangements. We show that in SiO2 these structural rearrangements are coupled to the creation or annihilation of defects, giving rise to very strong correlations of jumping atoms and defects.

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.