A Stellar Census of the Tucana-Horologium Moving Group

We report the selection and spectroscopic confirmation of 129 new late-type (SpT = K3-M6) members of the Tucana-Horologium moving group, a nearby (d similar to 40 pc), young (tau similar to 40 Myr) population of comoving stars. We also report observations for 13 of the 17 known Tuc-Hor members in this spectral type range, and that 62 additional candidates are likely to be unassociated field stars; the confirmation frequency for new candidates is therefore 129/191 = 67%. We have used radial velocities, Ha emission, and Li-6708 absorption to distinguish between contaminants and bona fide members. Our expanded census of Tuc-Hor increases the known population by a factor of similar to 3 in total and by a factor of similar to 8 for members with SpT >= K3, but even so, the K-M dwarf population of Tuc-Hor is still markedly incomplete. Our expanded census allows for a much more detailed study of Tuc-Hor than was previously feasible. The spatial distribution of members appears to trace a two-dimensional sheet, with a broad distribution in X and Y, but a very narrow distribution (+/- 5 pc) in Z. The corresponding velocity distribution is very small, with a scatter of +/- 1.1 km s(-1) about the mean UVW velocity for stars spanning the entire 50 pc extent of Tuc-Hor. We also show that the isochronal age (tau similar to 20-30 Myr) and the lithium depletion boundary age (tau similar to 40 Myr) disagree, following the trend in other pre-main-sequence populations for isochrones to yield systematically younger ages. The H alpha emission line strength follows a trend of increasing equivalent width with later spectral type, as is seen for young clusters. We find that moving group members have been depleted of measurable lithium for spectral types of K7.0-M4.5. None of our targets have significant infrared excesses in the WISE W3 band, yielding an upper limit on warm debris disks of F < 0.7%. Finally, our purely kinematic and color-magnitude selection procedure allows us to test the efficiency and completeness for activity-based selection of young stars. We find that 60% of K-M dwarfs in Tuc-Hor do not have ROSAT counterparts and would have been omitted in X-ray-selected samples. In contrast, GALEX UV-selected samples using a previously suggested criterion for youth achieve completeness of 77% and purity of 78%, and we suggest new SpT-dependent selection criteria that will yield > 95% completeness for tau similar to 40 Myr populations with GALEX data available.

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.