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.

Maternal and Postweaning High-Fat Diets Disturb Hippocampal Gene Expression, Learning, and Memory Function

We tested the hypothesis that excess saturated fat consumption during pregnancy, lactation, and/or postweaning alters the expression of genes mediating hippocampal synaptic efficacy and impairs spatial learning and memory in adulthood. Dams were fed control chow or a diet high in saturated fat before mating, during pregnancy, and into lactation. Offspring were weaned to either standard chow or a diet high in saturated fat. The Morris Water Maze was used to evaluate spatial learning and memory. Open field testing was used to evaluate motor activity. Hippocampal gene expression in adult males was measured using RT-PCR and ELISA. Offspring from high fat-fed dams took longer, swam farther, and faster to try and find the hidden platform during the 5-day learning period. Control offspring consuming standard chow spent the most time in memory quadrant during the probe test. Offspring from high fat-fed dams consuming excess saturated fat spent the least. The levels of mRNA and protein for brain-derived neurotrophic factor and activity-regulated cytoskeletal-associated protein were significantly decreased by maternal diet effects. Nerve growth factor mRNA and protein levels were significantly reduced in response to both maternal and postweaning high-fat diets. Expression levels for the N-methyl-D-aspartate receptor (NMDA) receptor subunit NR2B as well as synaptophysin were significantly decreased in response to both maternal and postweaning diets. Synaptotagmin was significantly increased in offspring from high fat-fed dams. These data support the hypothesis that exposure to excess saturated fat during hippocampal development is associated with complex patterns of gene expression and deficits in learning and memory.