Growth strategy determines the memory and structural properties of brain networks

The interplay between structure and function affects the emerging properties of many natural systems. Here we use an adaptive neural network model that couples activity and topological dynamics and reproduces the experimental temporal profiles of synaptic density observed in the brain. We prove that the existence of a transient period of relatively high synaptic connectivity is critical for the development of the system under noise circumstances, such that the resulting network can recover stored memories. Moreover, we show that intermediate synaptic densities provide optimal developmental paths with minimum energy consumption, and that ultimately it is the transient heterogeneity in the network that determines its evolution. These results could explain why the pruning curves observed in actual brain areas present their characteristic temporal profiles and they also suggest new design strategies to build biologically inspired neural networks with particular information processing capabilities. (C) 2021 The Author(s). Published by Elsevier Ltd.

Automated summary

The study shows that a transient period of relatively high synaptic connectivity is critical for the development of the system under noise circumstances, enabling the recovery of stored memories. Intermediate synaptic densities provide optimal developmental paths with minimum energy consumption, and the transient heterogeneity in the network ultimately determines its evolution.