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.
