Robust spatial memory maps encoded in networks with transient connections

TL;DR

This paper presents a novel algebraic topology-based model demonstrating how the hippocampus maintains a stable cognitive map despite rapid synaptic changes, highlighting the role of network dynamics and multiple timescales in spatial memory.

Contribution

It introduces a new mathematical framework showing the emergent stability of spatial memory maps in transient neural networks, with implications for understanding memory retention and deterioration.

Findings

Cognitive maps are stable despite rapid synaptic turnover.

Model predicts how physiological parameters affect memory stability.

Simulating neuronal activity can compensate for synaptic loss.

Abstract

The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space---a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map remains transient: the synaptic connections in the hippocampus and in the downstream neuronal networks never cease to form and to deteriorate at a rapid rate. How can the brain maintain a robust, reliable representation of space using a network that constantly changes its architecture? Here, we demonstrate, using novel Algebraic Topology techniques, that cognitive map's stability is a generic, emergent phenomenon. The model allows evaluating the effect produced by specific physiological parameters, e.g., the distribution of connections' decay times, on the properties of the cognitive map as a whole. It also points out that spatial memory deterioration caused by weakening or excessive loss of the synaptic connections may be compensated by simulating the neuronal activity. Lastly, the model explicates functional importance of the complementary learning systems for processing spatial information at different levels of spatiotemporal granularity, by establishing three complementary timescales at which spatial information unfolds. Thus, the model provides a principal insight into how can the brain develop a reliable representation of the world, learn and retain memories despite complex plasticity of the underlying networks and allows studying how instabilities and memory deterioration mechanisms may affect learning process.