Self-Wiring of Neural Networks

TL;DR

This paper introduces a simple computational model simulating neural self-wiring in 2D systems, capturing key features of growth cone migration driven by chemotaxis and feedback mechanisms.

Contribution

It presents a novel, minimal model that reproduces essential behaviors of neural self-wiring observed in 2D experimental systems.

Findings

Model successfully mimics growth cone chemotaxis

Reproduces network formation patterns

Highlights role of feedback in neural wiring

Abstract

In order to form the intricate network of synaptic connections in the brain, the growth cones migrate through the embryonic environment to their targets using chemical communication. As a first step to study self-wiring, 2D model systems of neurons have been used. We present a simple model to reproduce the salient features of the 2D systems. The model incorporates random walkers representing the growth cones, which migrate in response to chemotaxis substances extracted by the soma and communicate with each other and with the soma by means of attractive chemotactic "feedback".