Computational Modeling of Dendritic Spine Morphology Effects on Synaptic Signal Propagation in 2D Neuronal Networks

Poster #: 136
Session/Time: A
Author: Gregory Pierpoint
Mentor: Alberto Musto, MD, Ph.D.
Co-Investigator(s): 1. Abheek Ritvik, Department of Biomedical & Translational Sciences 2. Samantha Smith, EVMS MD Program M1 Student
Research Type: Basic Science

Abstract

Introduction: Neural inflammation promotes a reactive remodeling in dendritic spine expression. Cumulative alterations in the functional neural network are thought to be mirrored by morphologic change. In Vivo models of TLE demonstrate aberrant post-synaptic change.

Methods: This study presents a MATLAB-based 2D neuronal model for investigating the impact of dendritic spine morphology on synaptic signal propagation. The framework comprises four key components: a dendrite generation function utilizing probabilistic branching and angular distributions, a compartmental modeling system that discretizes dendritic segments and spines into electrical units with unique resistance and capacitance properties, a spine distribution function based on predefined densities and morphological types, and a visualization module. Electrical properties are computed using series and parallel resistance calculations for dendrites and spines. While the current model focuses on steady-state analysis, it is designed to accommodate future integration of dynamic signal propagation models.

Results: This computational approach enables systematic exploration of how spine morphology variations, characterized by head diameter, neck diameter, and length, affect neuronal signal processing. The model provides a foundation for investigating structural-functional relationships in neurons, with potential applications in understanding both normal brain function and pathological conditions such as epilepsy.

Conclusion: By offering a quantitative framework for analyzing the interplay between dendritic spine geometry and signal transmission, this work contributes to the broader understanding of neuronal information processing and network dynamics.