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.

The Behavior Rating Inventory Of Executive Function - Self-Report Version (Brief-Sr) And The Delis-Kaplan Executive Function Sys

The assessment of executive functions is an area of study that has seen considerable development in recent years. Despite much research examining the validity of various measures of executive functions from both a direct and indirect format, little evidence exists in the extant literature evaluating the correspondence between these types of measures. The current study examined the extent of correspondence, comprising concurrent validity, between the Delis-Kaplan Executive Function System (D-KEFS) and the Behavior Rating Inventory of Executive Function ¿ Self-Report Version (BRIEF-SR). Participants included 30 undergraduate and high school students 18 years of age. Results indicated mixed evidence of concurrent validity between the two measures of executive functions. The findings obtained suggest both expected significant, negative correlation as well as lack of expected correlation between the measures. Suggestions for future research in the assessment of executive functions are discussed.