Version 1
: Received: 2 May 2024 / Approved: 3 May 2024 / Online: 3 May 2024 (07:03:08 CEST)
How to cite:
Montgomery, R. M. A Computational Model of Dendritic Growth Dynamics: Exploring Exponential and Logarithmic Scales in Neural Development. Preprints2024, 2024050143. https://doi.org/10.20944/preprints202405.0143.v1
Montgomery, R. M. A Computational Model of Dendritic Growth Dynamics: Exploring Exponential and Logarithmic Scales in Neural Development. Preprints 2024, 2024050143. https://doi.org/10.20944/preprints202405.0143.v1
Montgomery, R. M. A Computational Model of Dendritic Growth Dynamics: Exploring Exponential and Logarithmic Scales in Neural Development. Preprints2024, 2024050143. https://doi.org/10.20944/preprints202405.0143.v1
APA Style
Montgomery, R. M. (2024). A Computational Model of Dendritic Growth Dynamics: Exploring Exponential and Logarithmic Scales in Neural Development. Preprints. https://doi.org/10.20944/preprints202405.0143.v1
Chicago/Turabian Style
Montgomery, R. M. 2024 "A Computational Model of Dendritic Growth Dynamics: Exploring Exponential and Logarithmic Scales in Neural Development" Preprints. https://doi.org/10.20944/preprints202405.0143.v1
Abstract
The intricate architecture of dendritic arborization is fundamental to the formation and functionality of neural networks, serving as the primary site for synaptic integration and signal propagation. This study presents a pioneering computational model that simulates dendritic growth dynamics, employing exponential and logarithmic scaling over an extended developmental period of 720 days. This model offers a valuable tool for investigating the implications of diverse dendritic growth patterns on neural development, synaptic connectivity, and ultimately, cognitive functions. The computational framework incorporates biologically plausible parameters, allowing for the systematic exploration of dendritic branching patterns and their potential impact on neuronal information processing. By simulating exponential and logarithmic growth scales, the model captures the inherent complexity and diversity of dendritic morphologies observed in various neuronal populations across different brain regions. The findings from this study hold significant implications for our understanding of neural circuit assembly, synaptic integration, and the potential functional consequences of aberrant dendritic growth patterns observed in neurodevelopmental disorders. Furthermore, the model's ability to simulate extended developmental trajectories over a prolonged period of 720 days offers insights into the dynamic interplay between dendritic growth and synaptic pruning, which is crucial for the refinement and optimization of neural networks.
Keywords
Computational Model Applied Mahematics Dendritic Growth Dynamics Neural Development
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
