Representational Drift and Learning-Induced Stabilization in the Olfactory Cortex
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.
Cite
@article{arxiv.2412.13713,
title = {Representational Drift and Learning-Induced Stabilization in the Olfactory Cortex},
author = {Guillermo B. Morales and Miguel A. Muñoz and Yuhai Tu},
journal= {arXiv preprint arXiv:2412.13713},
year = {2024}
}