# Concurrence of form and function in developing networks and its role in synaptic pruning

## TL;DR

This paper explores how neural network structure and function co-develop through a feedback mechanism, leading to optimized memory performance and potential insights into synaptic pruning during brain development.

## Contribution

It introduces a model combining auto-associative networks with synaptic birth and death, revealing how structure-function interplay influences network behavior and development.

## Key findings

- Heterogeneous, dissasortative networks enhance memory performance
- Homogeneous networks fail at pattern retrieval
- Model aligns with experimental observations of early brain development

## Abstract

A fundamental question in neuroscience is how structure and function of neural systems are related. We study this interplay by combining a familiar auto-associative neural network with an evolving mechanism for the birth and death of synapses. A feedback loop then arises leading to two qualitatively different types of behaviour. In one, the network structure becomes heterogeneous and dissasortative, and the system displays good memory performance; furthermore, the structure is optimised for the particular memory patterns stored during the process. In the other, the structure remains homogeneous and incapable of pattern retrieval. These findings provide an inspiring picture of brain structure and dynamics, are compatible with experimental results on early brain development, and may help to explain synaptic pruning. Other evolving networks -- such as those of protein interaction -- might share the basic ingredients for this feedback loop and other questions, and indeed many of their structural features are as predicted by our model.