Persistent memories in transient networks

TL;DR

This paper investigates how transient neuronal networks can maintain stable spatial memories by demonstrating that a flickering network model can preserve a robust topological representation of space despite ongoing structural changes.

Contribution

It introduces a physiological model of a flickering neuronal network that encodes stable spatial maps despite network transience, addressing a key challenge in understanding neural representations.

Findings

Flickering networks can sustain topological spatial representations.

Transient network architecture does not impair spatial memory encoding.

The model demonstrates robustness of spatial maps despite neuronal turnover.

Abstract

Spatial awareness in mammals is based on an internalized representation of the environment, encoded by large networks of spiking neurons. While such representations can last for a long time, the underlying neuronal network is transient: neuronal cells die every day, synaptic connections appear and disappear, the networks constantly change their architecture due to various forms of synaptic and structural plasticity. How can a network with a dynamic architecture encode a stable map of space? We address this question using a physiological model of a "flickering" neuronal network and demonstrate that it can maintain a robust topological representation of space.