Representational Drift and Learning-Induced Stabilization in the Olfactory Cortex

TL;DR

This paper presents a biologically-realistic computational model of the olfactory cortex that explains how neural representations change over time and how learning stabilizes these representations, shedding light on the phenomenon of representational drift.

Contribution

The study introduces a model combining spontaneous synaptic fluctuations and STDP to explain representational drift and its stabilization through learning in the olfactory system.

Findings

Spontaneous synaptic fluctuations induce representational drift.

STDP during repeated stimuli reduces drift.

Model aligns with experimental observations of olfactory neural dynamics.

Abstract

The brain encodes external stimuli through patterns of neural activity, forming internal representations of the world. Recent experiments show that neural representations for a given stimulus change over time. However, the mechanistic origin for the observed "representational drift" (RD) remains unclear. Here, we propose a biologically-realistic computational model of the piriform cortex to study RD in the mammalian olfactory system by combining two mechanisms for the dynamics of synaptic weights at two separate timescales: spontaneous fluctuations on a scale of days and spike-time dependent plasticity (STDP) on a scale of seconds. Our study shows that, while spontaneous fluctuations in synaptic weights induce RD, STDP-based learning during repeated stimulus presentations can reduce it. Our model quantitatively explains recent experiments on RD in the olfactory system and offers a mechanistic explanation for the emergence of drift and its relation to learning, which may be useful to study RD in other brain regions.