Date Completed
Spring 2026
Abstract
The N-Methyl-D-Aspartate (NMDA) Receptor is fundamentally important to memory formation within the brain due to its control of calcium entry into the cell. In recent years, there has been an increased interest in long-term effects of NMDA blockages on the brain, due to the “re-wiring” of communication channels (synapses) between neurons. This project models the effects of NMDA blockages due to drugs such as Ketamine, and how the blocking of NMDA receptors affects firing rates, which can then be applied to studying long-term plasticity within the neural hierarchies. Using the Nest Online Simulator, a 50x50 grid of neurons was created with an 80:20 ratio of excitatory to inhibitory neurons. Halfway through the simulation, the synaptic weights of NMDA were set to 0, replicating a ketamine-induced shutdown. Throughout the trials, it was shown that NMDA inhibition leads to a rapid decline in network spiking activity due to lack of synaptic communication. The future goals of this project include investigating different models, parameters, and source code within NEST to create a model to best show long-term plasticity within neural hierarchies.
Document Type
Undergraduate Thesis
First Advisor
Mark Lyon
College or School
CEPS
Department or Program
Mathematics and Statistics
Degree Name
Bachelor of Science
Recommended Citation
Raetsch, Anya, "Computationally Modelling NMDA Blockages Within a Neural Network" (2026). UNH URC Open (2026 and after). 14.
https://scholars.unh.edu/urc_open/14