Topological stability of the hippocampal spatial map and synaptic transience

TL;DR

This paper explores how hippocampal networks can maintain stable spatial maps despite ongoing synaptic changes, using an algebro-topological modeling approach to demonstrate the emergence of stable cognitive maps from transient neural architectures.

Contribution

It introduces a novel algebro-topological modeling framework showing that stable spatial representations can arise in dynamic neural networks, addressing a key paradox in neural plasticity.

Findings

Stable cognitive maps can emerge from transient neural architectures.

Algebro-topological models explain long-term spatial stability.

Network plasticity does not preclude stable spatial encoding.

Abstract

Spatial awareness in mammals is based on internalized representations of the environment---cognitive maps---encoded by networks of spiking neurons. Although behavioral studies suggest that these maps can remain stable for long periods, it is also well-known that the underlying networks of synaptic connections constantly change their architecture due to various forms of neuronal plasticity. This raises a principal question: how can a dynamic network encode a stable map of space? In the following, we discuss some recent results obtained in this direction using an algebro-topological modeling approach, which demonstrate that emergence of stable cognitive maps produced by networks with transient architectures is not only possible, but may be a generic phenomenon.