Investigating the Role of Refractory Periods in Neuronal Network Dynamics Using SNNAP and MATLAB

The refractory period plays a crucial role in shaping neuronal excitability and network dynamics. This study examines how absolute and relative refractory periods influence spike timing in a three-neuron network simulated in SNNAP (Simulator for Neural Networks and Action Potentials) and more complex neural networks. The network consists of a Hodgkin-Huxley (HH) neuron exciting gi_6 and gi_7 integrate-and-fire neurons through weighted synapses. Unexpectedly, post-synaptic neurons fired during the hyperpolarization phase of the presynaptic neuron rather than at the peak of the action potential. By adjusting synaptic conductance (g_syn) and threshold reset parameters, we analyzed how refractory dynamics affect spike timing and oscillatory behavior. Further analysis extends to MATLAB simulations, where we model single neurons and more detailed networks using Hodgkin-Huxley and integrate-andfire equations, as well as making more accurate models. By systematically varying membrane capacitance (Cm), synaptic delay, and threshold adaptation, we examine how refractory periods influence neural synchronization. These results provide insights into how refractory periods regulate network excitability and oscillatory activity. Future directions include studying adaptive refractory mechanisms and synaptic plasticity to explore how neural circuits dynamically adjust their firing properties over time

A Transformer-Based Approach for Gene Discovery in Radiation Response Under Data-Sparse Conditions

This paper investigates the application of Geneformer, a transformer-based model, for identifying genes that cause transitions between radiation levels in data-sparse situations. Traditional differential gene expression (DGE) methods often face limitations when data availability is minimal. Preprocessing was done to leverage high-throughput single-cell RNA sequencing data to ensure accurate analysis of the genes responsible for transitions in irradiated cell states. Statistical techniques, including t-tests, Wilcoxon rank-sum, and logistic regression, were employed to rank gene expression across four radiation exposures (0, 10, 100, and 1000 mGy). The Geneformer transformer-based model was fine-tuned on the tokenized data with hyperparameter optimization. This yielded significant improvements in classification accuracy as validated by two-dimensional embedding representations and in-silico perturbation experiments. When both processes were tested on data subsets consisting of 1024, 256, and 128 cells, the finetuned Geneformer model consistently outperformed the traditional DGE method. Overall, the findings demonstrate how Geneformer detects subtle shifts in gene expression with high precision and reliably identifies key genetic drivers of radiation response, thereby offering a viable alternative to conventional DGE approaches in low-data environments

Obstacle Detection in Autonomous Robots

The Duckietown Project was created by the MIT graduate class in 2016, and has transformed into a worldwide program. The Duckietown platform uses a robot consisting of cameras, actuators, and April Tags to navigate a modular cityscape. This research focuses on developing autonomous robots that integrate internal and external sensors with image processing to interpret their surroundings. The research aims to develop an algorithm enabling the Duckiebot to detect obstacles within the Duckietown environment. Through this research, we utilize Docker, Linux, Python, and machine learning to detect colors and objects

Clientele Researching with Ivy League Potential

Ivy League Potential is a college admissions company that works with adolescents and their families to guide their process in achieving career goals. The focus of this intemship has been clientele research to help expand the company\u27s knowledge of all different interests to aid any client in gaining experience in certain fields. The research was centered around accessible career exploration. Over the past school year, the company introduced different Steps to create a final document of valuable resources and information in gaining experience as an interested adolescent. The goal is to create an Ivy League Potential sponsored accessible program to assist in gaining experience. While the project has only passed phase one of the program, the goal of this business project is to explore different researching techniques

Analyzing localized heating in Deep Brain Stimulation leads under radiofrequency exposure

MRI is widely used in medical diagnostics due to its superior tissue contrast. However, patients with active implantable medical devices (AIMDs), such as deep brain stimulation (DBS) systems, face risks during MRI. DBS is an AIMD commonly used for neurological conditions, and many patients require MRI scans. During MRI, radiofrequency (RF) field energy can couple with the conductive DBS lead, increasing the specific absorption rate (SAR) of RF energy in surrounding tissues. This interaction may cause localized heating at the electrode tip, posing a safety concern. Heating depends on the shape, orientation, and length of the leads, and configuring the extracranial portion of DBS leads into concentric loops near the lead insertion point has been shown to reduce heating. In this study, realistic lead trajectories with and without loops were modeled to assess their impact on RF heating by minimizing exposure to the maximum tangential electric field. Using the validated transfer function of a commercially available Boston Scientific DBS system, tangential electric fields calculated in ANSYS HFSS along the lead trajectories were used to predict RF heating at the electrode tip. This work highlights the importance of optimizing DBS trajectories to significantly reduce RF heating and enhance patient safety

SBHC’s Impact on Chicago Adolescents in the Area of Substance Use

This study examines the correlation between education and healthcare through the lens of School-Based Health Centers (SBHCs) in Chicago, exploring how these facilities may reflect neighborhood inequality. We analyze how SBHCs serve as indicators of the collective socioeconomic status and social cohesion of their surrounding communities, highlighting disparities among Chicago’s neighborhoods. Recent studies suggest that SBHCs play a crucial role in improving healthcare outcomes and reducing income-related disparities To evaluate this premise, we conducted focus groups, questionnaires (i.e., ORIC, AIM, FIM, and IAM), and demographic surveys with healthcare professionals working at Erie Health and Rush University Health SBHCs across Chicago. This approach allows us to document and analyze variations in resource allocation, service delivery, and health outcomes among these two health center operators. From the survey, we noticed that most Rush SBHC sites have basic resources, such as access to nurse practitioners, but some did not meet the higher-level needs. While some SBH sites lacked screening methods, others had multiple options. This indicates that, in differen areas, SBHCs under the same company can provide varying services. This project aims t provide insight into how SBHCs mirror broader patterns of urban inequality

Computational analysis of TEMPO derivatives catholyte on redox-couple potential

This paper aims to find and evaluate optimal catholytes materials for Aqueous Organic Redox Flow Batteries (AORFBs) through using Density Functional Theory (DFT) calculation and other techniques. DFT is an atomistic modeling method that can provide accurate performance predictions of electrodes, electrolyte and membranes, significantly reducing experimental time and cost. The organic material I chose to focus on is 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) and its derivatives containing electron-withdrawing groups. TEMPO compounds often face challenges including insufficient solubility and low redox potential in AORFB applications so this study finds noble derivatives and performs atomistic modeling to observe different derivatives\u27 effect on redox potential

BSU

https://digitalcommons.imsa.edu/black_history_month_read_in/1002/thumbnail.jp

International Students Science Fair

IMSA hosted the 14th annual International student science fair in 2018. The five-day event addressed three critical global challenges that transcend national boundaries: water, hunger, and energy. Guided by the theme “Significantly Influencing Life on Our Planet Through Cooperation and Collaboration,” IMSA reaffirms its commitment to advancing knowledge and innovation as the world’s leading teaching and learning laboratory, working for the prosperity and security of both present and future generations.https://digitalcommons.imsa.edu/homepage/1073/thumbnail.jp

Towards Understanding Large Language Models for Multilingual Semantic Encoding

Natural Language Processing (NLP) has witnessed significant advancements with the emergence of large language models (LLM) capable of understanding and generating human-like text. However, there remains a critical need to explore and understand their efficiency and effectiveness, especially in processing languages beyond English. This study aims to evaluate the efficiency of various large language models in capturing semantic meaning across English, German, and Spanish sentences. Principal Component Analysis (PCA) is utilized to identify important weights for understanding semantics. Through further experimentation with various sentence structures, we aim to identify factors contributing to the effectiveness of certain models. By pinpointing the strengths and weaknesses of different models, we aim to advance NLP research for multilingual applications