My project is titled An Investigation of Learning in Networks of Spinal Cord Neurons. My inspiration for this project came from the beginnings of my involvement with the Neural Engineering (NE) Lab at GMU. When I was first involved with the NE Lab, I observed other students culturing neurons from the prefrontal cortex of embryonic mice, to characterize networks from the brain. When I had enough exposure in the lab, I asked the Principle Investigator (Dr. Nathalia Peixoto) if I could use the bodies of the pups to attempt at procuring networks of spinal cord neurons. It was my goal to get experience working in a wet lab environment, and also to increase the utility of the sacrificed animals (in this procedure, the entire body of the mouse pups is usually discarded). After getting approval, I successfully procured the networks, which inspired me to focus my research on spinal cord neurons. The lab had already demonstrated learning in networks of neurons from the prefrontal cortex, it made sense for me to try to reproduce these same results with a different type of neuron from the central nervous system.
During this project, the responsibilities of myself as well as my team varied widely depending on the project’s progression. Initially, preparations had to be made for the procedure of obtaining neurons from neonatal mice. After the cultures were obtained, they needed to be ‘fed’ with growth media exchanges three times a week. After approximately three weeks, the cultures then needed to be tested for electric activity. Upon discovery of an electrically-active network, the network needed to be stimulated to measure its response (this was how learning was observed and measured). Finally, after enough networks were stimulated, the data needed to be processed in multiple ways to adequately quantify the responses. These responsibilities ensured that no two weeks were the same.
This project is the culmination of my undergraduate research experience, and it showcases my ability to lead a team to accomplish a project. The project is expected to be finished with the completion of data processing in the spring of 2018.