Armaan Bhojwani (1, 4), Julia Tomaszewska (2,4), Thalia Inui (3,4), Nitsueh Kebere (4), Dr. Marianne Bezaire (4).
The neurotransmitter norepinephrine (NE) has been shown to have a significant effect on Prefrontal Cortex (PFC) activity and Working Memory (WM) performance. Low levels of NE have been observed to increase delay-rated firing and improve WM, while high levels of NE have been observed to decrease delay-rated firing and impair WM. The purpose of this study was to create a computational model capable of simulating the effect of different concentrations of NE on WM using a spiking neural network model of the PFC. NE affects WM performance by binding to the alpha-1 and alpha-2 receptors. alpha-1 receptors are activated at high NE levels and decrease neuronal firing rates, while the alpha-2 receptors activate at basal NE levels and increase neuronal firing rates. The effects of NE binding to these receptors were approximated and modeled by multiplying the biases and gains of the neural network by a scalar based on the percentage of each receptor type active at a specific NE level. The model was run for NE concentrations ranging from 0 nM to 1000 nM using steps of 10 nM. For each step, 1000 trials of the neural network simulation were run, and the average neuronal firing rate and WM performance at each NE level was recorded and graphed. The model developed in this study can be utilized to investigate and develop treatments for diseases involving defective NE levels such as Attention Deficit/Hyperactivity Disorder (ADHD), Post Traumatic Stress Disorder (PTSD), and Parkinson's Disease by simulating how these disorders or their potential treatments would impact PFC function and WM. This model can be improved when more information on this topic is made available, such as more specific numerical data describing the effects of NE acting on alpha-receptors and consistent results about the exact role of beta receptors in WM.