Telomeres: Linking stress and survival, ecology and evolution.

Telomeres are protective structures at the ends of eukaryotic chromosomes. The loss of telomeres through cell division and oxidative stress is related to cellular aging, organismal growth and disease. In this way, telomeres link molecular and cellular mechanisms with organismal processes, and may explain variation in a number of important life-history traits. Here, we discuss how telomere biology relates to the study of physiological ecology and life history evolution. We emphasize current knowledge on how telomeres may relate to growth, survival and lifespan in natural populations. We finish by examining interesting new connections between telomeres and the glucocorticoid stress response. Glucocorticoids are often employed as indices of physiological condition, and there is evidence that the glucocorticoid stress response is adaptive. We suggest that one way that glucocorticoids impact organismal survival is through elevated oxidative stress and telomere loss. Future work needs to establish and explore the link between the glucocorticoid stress response and telomere shortening in natural populations. If a link is found, it provides an explanatory mechanism by which environmental perturbation impacts life history trajectories.

Ecology, sexual selection and speciation

The spectacular diversity in sexually selected traits among animal taxa has inspired the hypothesis that divergent sexual selection can drive speciation. Unfortunately, speciation biologists often consider sexual selection in isolation from natural selection, even though sexually selected traits evolve in an ecological context: both preferences and traits are often subject to natural selection. Conversely, while behavioural ecologists may address ecological effects on sexual communication, they rarely measure the consequences for population divergence. Herein, we review the empirical literature addressing the mechanisms by which natural selection and sexual selection can interact during speciation. We find that convincing evidence for any of these scenarios is thin. However, the available data strongly support various diversifying effects that emerge from interactions between sexual selection and environmental heterogeneity. We suggest that evaluating the evolutionary consequences of these effects requires a better integration of behavioural, ecological and evolutionary research.

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.