Year: 2024
Type: preprint
Abstract: Our representations of the world need to be stable enough to support general knowledge but flexible enough to incorporate new information as our environment changes. How does the human brain manage this stability-plasticity trade-off? We analyzed a large dataset in which participants viewed objects embedded in thousands of natural scenes across many fMRI sessions. Semantic item representations were located by jointly leveraging a voxelwise encoding model to find reliable item representations and a word-embedding model to evaluate semantic content. Within the medial temporal lobe, semantic item representations in hippocampal subfield CA1, parahippocampal cortex, and perirhinal cortex gradually drifted across a period of multiple months. Whole-brain analyses revealed a gradient of plasticity in the temporal lobe, with drift more evident in anterior than posterior areas. On short timescales, rapid plasticity was observed only in parahippocampal cortex, such that item co-occurrence statistics warped item representations within a single session. Together, the results suggest that the brain solves the stability-plasticity trade-off through a gradient of plasticity across semantic regions.
Authors: S.H. Solomon, K. Kay, A.C. Schapiro
Institutions: University of Pennsylvania, University of Minnesota
Language: English
Topic: Memory and Neural Mechanisms
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funder: National Science Foundation
Awards: CRCNS Research Proposal: Collaborative Research: Evaluating Machine Learning Architectures Using a Massive Benchmark Dataset of Brain Responses to Natural Scenes, CRCNS Research Proposal: Collaborative Research: Evaluating Machine Learning Architectures Using a Massive Benchmark Dataset of Brain Responses to Natural Scenes
OpenAlex ID: W4391650433