preprint · arXiv (Cornell University) · 2023
Memory Encoding Model
Huzheng Yang, James C. Gee, Jianbo Shi
Cited by 1Open access
Abstract
We explore a new class of brain encoding model by adding memory-related information as input. Memory is an essential brain mechanism that works alongside visual stimuli. During a vision-memory cognitive task, we found the non-visual brain is largely predictable using previously seen images. Our Memory Encoding Model (Mem) won the Algonauts 2023 visual brain competition even without model ensemble (single model score 66.8, ensemble score 70.8). Our ensemble model without memory input (61.4) can also stand a 3rd place. Furthermore, we observe periodic delayed brain response correlated to 6th-7th prior image, and hippocampus also showed correlated activity timed with this periodicity. We conjuncture that the periodic replay could be related to memory mechanism to enhance the working memory.
Cite this paper
Yang, H., Gee, J. C., & Shi, J. (2023). Memory Encoding Model. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2308.01175
Yang, Huzheng, et al. “Memory Encoding Model.” arXiv (Cornell University), 2023, https://doi.org/10.48550/arxiv.2308.01175.
Yang, Huzheng, James C. Gee, and Jianbo Shi. 2023. “Memory Encoding Model.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.2308.01175.
Yang, H., Gee, J.C. and Shi, J. (2023) ‘Memory Encoding Model’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.2308.01175.
@misc{yang2023memory,
title = {{Memory Encoding Model}},
author = {Yang, Huzheng and Gee, James C. and Shi, Jianbo},
journal = {arXiv (Cornell University)},
year = {2023},
doi = {10.48550/arxiv.2308.01175}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.