Project overview
Developed a Python-based computational model to investigate how dopamine transporter activity may affect synaptic signalling in ADHD. The project simulated inhibitory interactions between dopaminergic neurons and compared healthy, ADHD-like and stimulant-treated conditions.
A computational framework for exploring how altered dopamine reuptake may affect synaptic signalling in ADHD and how stimulant treatment may partially restore neuronal activity.
Approach
Built on existing Hodgkin–Huxley, dopaminergic-neuron and biophysical circuit models, using a two-neuron inhibitory network as the starting point. The model incorporated dopamine transporter (DAT) activity, D2 receptor-mediated signalling and potassium-channel conductance to represent changes in synaptic dopamine dynamics. Simulations were implemented in Python using NumPy, SciPy and Matplotlib.
Outcome
The ADHD-like simulation produced a lower and flatter synaptic current, with a peak of approximately 2 nA compared with around 6 nA in the healthy model. Simulated stimulant treatment increased the current to nearly 3 nA, indicating partial improvement but not a full return to the healthy-model range.