The Role of Antennapedia in Anteroposterior Patterning of the Drosophila Melanogaster Ventral Nervous System

A major unresolved question in developmental neurobiology is how the nervous system is adapted to the specific needs of the organism at different life stages. In the holometabolous insect Drosophila melanogaster, the larval ventral nervous system (VNS) is comprised of similar repeating segments, as opposed to the adult VNS, which varies greatly from segment to segment both in number and types of neurons. The adult-specific neurons of each segment are generated by 25 distinct types of neuronal progenitor cells called neuroblasts (NBs) that appear in a stereotyped array (Truman et al., 2004). Each NB divides repeatedly to produce a distinct set of daughter cells termed a lineage, which is bilaterally symmetric but present to varying degrees in each segment. These daughter cells can be distinguished by their position within the nervous system as well as by their axonal projections. Each of the 25 NBs produces neurons; if both daughter cells are present in a lineage then both sibling populations survived, whereas if only one projection is seen cell death occurred, leaving a hemilineage (half lineage). In some lineages, the same sibling type survives in all segments in which the lineage appears, but in others, the surviving sibling type varies across segments, resulting in a different morphology for the same lineage in different segments. How are these differences in survival and morphology controlled? The Hox genes provide positional information for developing structures along the anterior-posterior (AP) axis of animals. They encode transcription factors, thereby controlling the activity of genes down stream. In the postembryonic VNS, each NB lineage features its own characteristic expression pattern of Hox genes Antp and Ubx, which can vary from segment-to-segment, and can thereby cause variation in the number of neural cells and axonal projections that survive. This study defines the wild-type expression pattern of Antp and elucidates the role of Antp in gain of function studies. These studies are possible due to the MARCM (Mosaic Analysis with a Repressible Cell Marker) method, which allows the genetically manipulated cells to be specifically labeled in an otherwise normal, unlabeled organism. The results indicate that Antp is expressed in a segment-, lineage-, and hemilineage-specific manner. Antp is sufficient for both anterior and posterior transformations of particular lineages, including promotion of cell death and/or survival as well as axon guidance.

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.