Modeling of interstitial branching of axonal networks

TL;DR

This paper presents a mathematical model for interstitial axonal branching, emphasizing the role of guidance molecules, and demonstrates through simulations that the model replicates neural network growth patterns.

Contribution

It introduces a novel mathematical framework for modeling interstitial axonal branching influenced by guidance molecules during neural development.

Findings

Model accurately simulates axonal branching behavior.

Simulations produce neural network-like structures.

Guidance molecules significantly affect branching patterns.

Abstract

A single axon can generate branches connecting with plenty synaptic targets. Process of branching is very important for making connections in central nervous system. The interstitial branching along primary axon shaft occurs during nervous system development. Growing axon makes pause in its movement and leaves active points behind its terminal. The new branches appear from these points. We suggest mathematical model to describe and investigate neural network branching process. The model under consideration describes neural network growth in which the concentration of axon guidance molecules manages axon's growth. We model the interstitial branching from axon shaft. Numerical simulations show that in the model framework axonal networks are similar to neural network.