Refactoring Synaptic Behavior in HNN-Core

This project focuses on improving the computational efficiency and biological realism of synaptic modeling in HNN-Core by refactoring how synapses are placed, simulated, and recorded within neuron models. Currently, synapses are instantiated at fixed locations across neuronal sections, including inactive ones, leading to unnecessary computational overhead and reduced clarity in simulation outputs. The proposed approach introduces parameterized synapse placement, ensuring that synapses are only created at locations where neurons receive thalamic or cortical input. The refactor will also restrict synaptic current recording to active synapses, reducing memory usage and improving interpretability of results. Additional enhancements include updating existing network models, ensuring backward compatibility, and developing validation strategies to maintain numerical and biophysical consistency. To further improve usability, the project will incorporate visualization tools for analyzing synaptic currents and placement patterns. Time permitting, biologically realistic variability in synapse positioning will be introduced to better reflect neural heterogeneity. While this work focuses on synaptic modeling, my recent contributions to HNN-Core have also involved improving testing infrastructure and code reliability, providing additional insight into maintaining correctness and stability in scientific simulation software. The outcome of this project will be a more efficient, interpretable, and scientifically robust synaptic modeling framework within HNN-Core.