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.

When conditioned responses "fire back": bidirectional cross-activation creates learning opportunities in synesthesia

In grapheme-color synesthesia, the letter "c" printed in black may be experienced as red, but typically the color red does not trigger the experience of the letter "c." Therefore, at the level of subjective experience, cross-activation is usually unidirectional. However, recent evidence from digit-color synesthesia suggests that at an implicit level bidirectional cross-activation can occur. Here we demonstrate that this finding is not restricted to this specific type of synesthesia. We introduce a new method that enables the investigation of bidirectionality in other types of synesthesia. We found that a group of grapheme-color synesthetes, but not a control group, showed a startle in response to a color-inducing grapheme after a startle response was conditioned to the specific corresponding color. These results implicate that when the startle response was associated with the real color an association between shock and the grapheme was also established. By this mechanism (i.e. implicit cross-activation) the conditioned response to the real color generalized to the synesthetic color. We suggest that parietal brain areas are responsible for this neural backfiring.

Performance-Based Engineering Framework for Earthquake and Fire Following Earthquake

The objective for this thesis is to outline a Performance-Based Engineering (PBE) framework to address the multiple hazards of Earthquake (EQ) and subsequent Fire Following Earthquake (FFE). Currently, fire codes for the United States are largely empirical and prescriptive in nature. The reliance on prescriptive requirements makes quantifying sustained damage due to fire difficult. Additionally, the empirical standards have resulted from individual member or individual assembly furnace testing, which have been shown to differ greatly from full structural system behavior. The very nature of fire behavior (ignition, growth, suppression, and spread) is fundamentally difficult to quantify due to the inherent randomness present in each stage of fire development. The study of interactions between earthquake damage and fire behavior is also in its infancy with essentially no available empirical testing results. This thesis will present a literature review, a discussion, and critique of the state-of-the-art, and a summary of software currently being used to estimate loss due to EQ and FFE. A generalized PBE framework for EQ and subsequent FFE is presented along with a combined hazard probability to performance objective matrix and a table of variables necessary to fully implement the proposed framework. Future research requirements and summary are also provided with discussions of the difficulties inherent in adequately describing the multiple hazards of EQ and FFE.

Concept Fire Truck

A prototype Concept Fire Truck was designed using Autodesk Inventor 3D Design Software. Various pictures of old-time and toy fire trucks were utilized for this project. The prototype was printed using a 3D printer to verify that all parts of the truck would fit and work as intended.